miχPODS: New baseline#
TODO
more intake usage
why does the stability diagram load multiple times
Is there an error in ONI phase labeling for the newer runs, presumably SST changed some.
Add diagnostic for convective “adjustment” or
diff == 1hvplot defaults
turn off scroll zoom
copy over presentation stuff
Notes#
This notebook compares:
TAO
“old” baseline with KD=1e-5, KV=2e-4 (NCAR/MOM6#238)
old baseline +
kpp.lmd.004withKPP ν0=2.5, Ric=0.2, Ri0=0.5new baseline :
KD=0, KV=0new baseline :
kpp.lmd.004withKPP ν0=2.5, Ric=0.2, Ri0=0.5new baseline :
kpp.lmd.005withKPP ν0=2.5, Ric=0.3, Ri0=0.5
Summary#
Turning down the background visc by a factor of 40, (2e-4 → 5e-5 m2/s)
sharpens the EUC relative to TAO
changes the stability diagram for El-Nino warming phase.
Using
Ri_c=0.2, so shallower KPP surface layer, is key as Dan mentioned.
Catalog display#
Show code cell source Hide code cell source
%load_ext rich
# MOM6 run catalog
catalog = {
"baseline": (
"baseline",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods",
),
"epbl": ("ePBL", "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods"),
"kpp.lmd.002": (
"KPP Ri0=0.5",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods",
),
"kpp.lmd.003": (
"KPP Ri0=0.5, Ric=0.2,",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods",
),
"kpp.lmd.004": (
"KPP ν0=2.5, Ric=0.2, Ri0=0.5",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods",
),
"baseline.N150": (
"baseline N=150",
"gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods",
),
"kpp.lmd.004.N150": (
"KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150",
"gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods",
),
"new_baseline.hb": (
"KD=0, KV=0",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb",
),
"new_baseline.kpp.lmd.004": (
"KPP ν0=2.5, Ric=0.2, Ri0=0.5",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
),
"new_baseline.kpp.lmd.005": (
"KPP ν0=2.5, Ri0=0.5",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
),
}
catalog
The rich extension is already loaded. To reload it, use:
%reload_ext rich
{ 'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'), 'epbl': ('ePBL', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods'), 'kpp.lmd.002': ( 'KPP Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods' ), 'kpp.lmd.003': ( 'KPP Ri0=0.5, Ric=0.2,', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods' ), 'kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods' ), 'baseline.N150': ('baseline N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods'), 'kpp.lmd.004.N150': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods' ), 'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'), 'new_baseline.kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods' ), 'new_baseline.kpp.lmd.005': ( 'KPP ν0=2.5, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods' ) }
Setup#
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%load_ext autoreload
%load_ext rich
%load_ext watermark
import math
import warnings
import cf_xarray as cfxr
import distributed
import holoviews as hv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
from datatree import DataTree
import xarray as xr
%aimport pump
from pump import mixpods
hv.notebook_extension("bokeh")
cfxr.set_options(warn_on_missing_variables=False)
xr.set_options(keep_attrs=True, display_expand_data=False)
plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140
%watermark -iv
Show code cell output Hide code cell output
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
from distributed.utils import tmpfile
pandas : 1.5.3
sys : 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
xarray : 2023.3.0
json : 2.0.9
tqdm : 4.65.0
cf_xarray : 0.8.0
pump : 1.0+247.g1f1c5e1.dirty
numpy : 1.23.5
distributed : 2023.3.0
dask_jobqueue: 0.7.3
matplotlib : 3.7.1
holoviews : 1.15.4
Show code cell source Hide code cell source
if "client" in locals():
client.close()
del client
if "cluster" in locals():
cluster.close()
import ncar_jobqueue
cluster = ncar_jobqueue.NCARCluster(
local_directory="/local_scratch/pbs.$PBS_JOBID/dask/spill",
)
cluster.adapt(minimum_jobs=1, maximum_jobs=4)
client = distributed.Client(cluster)
client
Show code cell output Hide code cell output
Client
Client-6366c295-d427-11ed-8f74-3cecef1b11fa
| Connection method: Cluster object | Cluster type: dask_jobqueue.PBSCluster |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status |
Cluster Info
PBSCluster
93c50c25
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status | Workers: 0 |
| Total threads: 0 | Total memory: 0 B |
Scheduler Info
Scheduler
Scheduler-e99aee42-150c-4812-943c-e5e9a2e93ac8
| Comm: tcp://10.12.206.54:34663 | Workers: 0 |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status | Total threads: 0 |
| Started: Just now | Total memory: 0 B |
Workers
Read#
Always read TAO#
tao_gridded = mixpods.load_tao()
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
Subset Catalog, build tree#
catalog_sub = {
k: catalog[k]
for k in catalog.keys()
if k == "kpp.lmd.004" or ("baseline" in k and "150" not in k)
}
display(catalog_sub)
{ 'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'), 'kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods' ), 'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'), 'new_baseline.kpp.lmd.004': ( 'KPP ν0=2.5, Ric=0.2, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods' ), 'new_baseline.kpp.lmd.005': ( 'KPP ν0=2.5, Ri0=0.5', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods' ) }
datasets = {
"TAO": tao_gridded,
# "MITgcm": mitgcm,
}
%autoreload
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=FutureWarning)
for short_name, (long_name, folder) in tqdm.tqdm(catalog_sub.items()):
datasets.update(
{
short_name: mixpods.load_mom6_sections(folder).assign_attrs(
{"title": long_name}
)
}
)
100%|██████████| 5/5 [00:31<00:00, 6.39s/it]
datasets["les"] = les["0.0.-140.oct.average.month"].to_dataset()
# Offset LES to work with slicing below
datasets["les"]["time"] = datasets["les"]["time"] + pd.Timedelta(days=25 * 365)
tree = DataTree.from_dict(datasets)
tree
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- time: 212302
- depth: 61
- depthchi: 6
- deepest(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- description :
- Deepest depth with a valid observation
- units :
- m
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - depth(depth)float64-300.0 -295.0 -290.0 ... -5.0 0.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255., -250., -245., -240., -235., -230., -225., -220., -215., -210., -205., -200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0.]) - eucmax(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 17 graph layers Data type float64 numpy.ndarray - latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- mld(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- long_name :
- $z_{MLD}$
- units :
- m
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 20 graph layers Data type float64 numpy.ndarray - reference_pressure()int640
array(0)
- shallowest(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - time(time)datetime64[ns]1996-01-01 ... 2020-03-20T21:00:00
array(['1996-01-01T00:00:00.000000000', '1996-01-01T01:00:00.000000000', '1996-01-01T02:00:00.000000000', ..., '2020-03-20T19:00:00.000000000', '2020-03-20T20:00:00.000000000', '2020-03-20T21:00:00.000000000'], dtype='datetime64[ns]') - zeuc(depth, time)float64dask.array<chunksize=(42, 132856), meta=np.ndarray>
Array Chunk Bytes 98.80 MiB 42.57 MiB Shape (61, 212302) (42, 132856) Dask graph 4 chunks in 3 graph layers Data type float64 numpy.ndarray - depthchi(depthchi)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- dcl_mask(depth, time)booldask.array<chunksize=(61, 33130), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 12.35 MiB 5.82 MiB Shape (61, 212302) (61, 100000) Dask graph 3 chunks in 45 graph layers Data type bool numpy.ndarray - oni(time)float32-0.9 -0.9 -0.9 -0.9 ... nan nan nan
array([-0.9, -0.9, -0.9, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'La-Nina warm' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina warm', 'La-Nina warm', 'La-Nina warm', ..., '____________', '____________', '____________'], dtype='<U12')
- N2(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $N^2$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - N2T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $N^2_T$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - Ri(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri_g$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - Rig_T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray - S(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 41
- generic_name :
- sal
- long_name :
- SALINITY (PSU)
- name :
- S
- standard_name :
- sea_water_salinity
- units :
- PSU
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - S2(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $S^2$
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - T(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- f10.2
- epic_code :
- 20
- generic_name :
- temp
- long_name :
- TEMPERATURE (C)
- name :
- T
- standard_name :
- sea_water_temperature
- units :
- C
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - dens(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $ρ$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - densT(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- description :
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- long_name :
- $ρ_T$
- standard_name :
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- units :
- kg/m3
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - lwnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
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- generic_name :
- qln
- long_name :
- NET LONGWAVE RADIATION
- name :
- LWN
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qlat(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 137
- generic_name :
- qlat
- long_name :
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- name :
- QL
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 210
- generic_name :
- qtot
- long_name :
- TOTAL HEAT FLUX
- name :
- QT
- units :
- W/M**2
- standard_name :
- surface_downward_heat_flux_in_sea_water
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - qsen(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 138
- generic_name :
- qsen
- long_name :
- SENSIBLE HEAT FLUX
- name :
- QS
- units :
- W m-2
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - swnet(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1495
- generic_name :
- sw
- long_name :
- NET SHORTWAVE RADIATION
- name :
- SWN
- units :
- W/M**2
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tau(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray - taux(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - tauy(time)float64dask.array<chunksize=(212302,), meta=np.ndarray>
- standard_name :
- surface_downward_y_stress
Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray - theta(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- description :
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- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray - u(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - v(time, depth)float32dask.array<chunksize=(33130, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray - wind_dir(time)float32dask.array<chunksize=(212302,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 410
- generic_name :
- long_name :
- WIND DIRECTION
- name :
- WD
- standard_name :
- wind_from_direction
- units :
- degrees
Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - pressure(depth)float64301.9 296.8 291.8 ... 5.028 -0.0
- standard_name :
- sea_water_pressure
- units :
- dbar
array([301.87732362, 296.84242473, 291.80764803, 286.77299352, 281.73846121, 276.70405112, 271.66976325, 266.63559761, 261.60155422, 256.56763308, 251.5338342 , 246.5001576 , 241.46660329, 236.43317126, 231.39986155, 226.36667414, 221.33360906, 216.30066632, 211.26784592, 206.23514788, 201.2025722 , 196.17011889, 191.13778797, 186.10557945, 181.07349333, 176.04152963, 171.00968835, 165.97796951, 160.94637311, 155.91489917, 150.8835477 , 145.8523187 , 140.82121218, 135.79022817, 130.75936665, 125.72862766, 120.69801119, 115.66751726, 110.63714587, 105.60689704, 100.57677078, 95.54676709, 90.51688599, 85.48712749, 80.4574916 , 75.42797832, 70.39858766, 65.36931965, 60.34017428, 55.31115157, 50.28225153, 45.25347416, 40.22481948, 35.1962875 , 30.16787822, 25.13959167, 20.11142784, 15.08338675, 10.0554684 , 5.02767282, -0. ]) - SA(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- standard_name :
- sea_water_absolute_salinity
- units :
- g/kg
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 5 graph layers Data type float64 numpy.ndarray - CT(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- standard_name :
- sea_water_conservative_temperature
- units :
- degC
- reference_scale :
- ITS-90
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 9 graph layers Data type float64 numpy.ndarray - α(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- units :
- 1/K
- standard_name :
- sea_water_thermal_expansion_coefficient
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray - β(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- units :
- kg/g
- standard_name :
- sea_water_haline_contraction_coefficient
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray - Tz(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- ℃/m
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - Sz(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- g/kg/m
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - chi(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - KT(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - eps(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Jq(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - shred2(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray - Rig(time, depth)float64dask.array<chunksize=(33130, 61), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray - sst(time)float64dask.array<chunksize=(33130,), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- units :
- degC
Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 4 graph layers Data type float64 numpy.ndarray - Tflx_dia_diff(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Rif(time, depthchi)float64dask.array<chunksize=(33130, 6), meta=np.ndarray>
- standard_name :
- flux_richardson_number
Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 52 graph layers Data type float64 numpy.ndarray - Jb(time, depthchi)float64dask.array<chunksize=(33130, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 49 graph layers Data type float64 numpy.ndarray
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (time: 212302, depth: 61, depthchi: 6) Coordinates: (12/19) deepest (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray> * depth (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0 eucmax (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray> latitude float32 0.0 longitude float32 -140.0 mld (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray> ... ... oni (time) float32 -0.9 -0.9 -0.9 -0.9 ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool True True True True ... False False False warm_mask (time) bool True True True True ... True True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 'La-Nina warm' ... '____________' Data variables: (12/38) N2 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> N2T (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> Ri (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> Rig_T (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> S (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> S2 (time, depth) float32 dask.array<chunksize=(33130, 61), meta=np.ndarray> ... ... shred2 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> Rig (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray> sst (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray> Tflx_dia_diff (time, depthchi) float64 nan nan nan nan ... nan nan nan Rif (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray> Jb (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray> Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- time: 533544
- zi: 27
- zl: 27
- nv: 2
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]1958-01-06T00:30:00 ... 2020-12-...
array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000', '1958-01-06T02:30:00.000000000', ..., '2020-12-05T21:30:00.000000000', '2020-12-05T22:30:00.000000000', '2020-12-05T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 4.07 MiB 68.44 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 4.07 MiB 68.44 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 13.74 MiB 230.98 kiB Shape (27, 533544) (27, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan ... -1.175 -1.175
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([ nan, nan, nan, ..., -1.175222, -1.175222, -1.175222], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_OBLdepth(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- ocean_vertical_y_viscosity
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N^2$
- units :
- s$^{-2}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(533544,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 109.91 MiB 1.80 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- flux_richardson_number
Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- baseline
<xarray.DatasetView> Dimensions: (time: 533544, zi: 27, zl: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 1958-01-06T00:30:00 ... 2020-12-0... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan ... -1.175 -1.175 -1.175 en_mask (time) bool False False False False ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool True True True True ... True True True True cool_mask (time) bool False False False False ... False False False enso_transition (time) <U12 '____________' ... 'La-Nina warm' Data variables: (12/38) KPP_OBLdepth (time) float32 dask.array<chunksize=(533544,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> Kd_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> Kv_u (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> Kv_v (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: baselinebaseline- time: 218424
- zi: 27
- zl: 27
- nv: 2
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]2003-01-07T00:30:00 ... 2028-01-...
array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000', '2003-01-07T02:30:00.000000000', ..., '2028-01-06T21:30:00.000000000', '2028-01-06T22:30:00.000000000', '2028-01-06T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 5.62 MiB 230.98 kiB Shape (27, 218424) (27, 8760) Dask graph 25 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
- KPP_N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of vertical shear used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Lateral diffusion tracer content tendency for T
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
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- time_avg_info :
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- units :
- kg/m3
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
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- units :
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Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
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- units :
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Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
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- units :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
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- units :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
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- long_name :
- $J_q^t$
Array Chunk Bytes 44.99 MiB 1.80 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
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- standard_name :
- sea_surface_temperature
- time_avg_info :
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- units :
- degC
Array Chunk Bytes 853.22 kiB 34.22 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 218424, zi: 27, zl: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-07T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 ... -2.5 -0.0 ... ... oni (time) float32 nan nan nan ... nan nan nan en_mask (time) bool False False ... False False ln_mask (time) bool False False ... False False warm_mask (time) bool True True True ... True True cool_mask (time) bool False False ... False False enso_transition (time) <U12 '____________' ... '_________... Data variables: (12/53) KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(218424,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5kpp.lmd.004- time: 534360
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]1958-01-01T00:30:00 ... 2018-12-...
array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000', '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000', '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
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- domain_decomposition :
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- long_name :
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- units :
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array(-140.)
- yh()float640.0625
- axis :
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- domain_decomposition :
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- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
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- domain_decomposition :
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- long_name :
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- units :
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array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
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array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 13.76 MiB 230.98 kiB Shape (27, 534360) (27, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 110.07 MiB 1.80 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (time: 534360, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 1958-01-01T00:30:00 ... 2018-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan nan ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'El-Nino warm' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KD=0, KV=0new_baseline.hb- time: 113880
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2015-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000', '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 2.93 MiB 230.98 kiB Shape (27, 113880) (27, 8760) Dask graph 13 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 23.46 MiB 1.80 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 444.84 kiB 34.22 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 113880, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2015-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan nan ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'El-Nino warm' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- time: 105120
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2014-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2014-12-31T21:30:00.000000000', '2014-12-31T22:30:00.000000000', '2014-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 2.71 MiB 230.98 kiB Shape (27, 105120) (27, 8760) Dask graph 12 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 21.65 MiB 1.80 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 410.62 kiB 34.22 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 105120, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2014-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan nan ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... '____________' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
Modify#
if "les" in tree:
tree["les"] = tree["les"].isel(z=slice(-2))
tree["les"]["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
if "micro" in locals():
tree.update(micro)
Post-process catalog subset#
tree = tree.sel(time=slice("2000", "2017"))
tree
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- time: 157800
- depth: 61
- depthchi: 6
- deepest(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- description :
- Deepest depth with a valid observation
- units :
- m
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - depth(depth)float64-300.0 -295.0 -290.0 ... -5.0 0.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255., -250., -245., -240., -235., -230., -225., -220., -215., -210., -205., -200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0.]) - eucmax(time)float64dask.array<chunksize=(98066,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 18 graph layers Data type float64 numpy.ndarray - latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- mld(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- long_name :
- $z_{MLD}$
- units :
- m
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(98066,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray - reference_pressure()int640
array(0)
- shallowest(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - time(time)datetime64[ns]2000-01-01 ... 2017-12-31T23:00:00
array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000', '2000-01-01T02:00:00.000000000', ..., '2017-12-31T21:00:00.000000000', '2017-12-31T22:00:00.000000000', '2017-12-31T23:00:00.000000000'], dtype='datetime64[ns]') - zeuc(depth, time)float64dask.array<chunksize=(42, 97792), meta=np.ndarray>
Array Chunk Bytes 73.44 MiB 31.34 MiB Shape (61, 157800) (42, 97792) Dask graph 4 chunks in 4 graph layers Data type float64 numpy.ndarray - depthchi(depthchi)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- dcl_mask(depth, time)booldask.array<chunksize=(61, 98066), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 9.18 MiB 5.70 MiB Shape (61, 157800) (61, 98066) Dask graph 2 chunks in 46 graph layers Data type bool numpy.ndarray - oni(time)float32-1.66 -1.66 -1.66 ... -0.87 -0.87
array([-1.66, -1.66, -1.66, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- N2(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $N^2$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - N2T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $N^2_T$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Ri(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri_g$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Rig_T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - S(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 41
- generic_name :
- sal
- long_name :
- SALINITY (PSU)
- name :
- S
- standard_name :
- sea_water_salinity
- units :
- PSU
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - S2(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $S^2$
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - T(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- f10.2
- epic_code :
- 20
- generic_name :
- temp
- long_name :
- TEMPERATURE (C)
- name :
- T
- standard_name :
- sea_water_temperature
- units :
- C
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - dens(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $ρ$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - densT(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- description :
- density using T, S
- long_name :
- $ρ_T$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - lwnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1136
- generic_name :
- qln
- long_name :
- NET LONGWAVE RADIATION
- name :
- LWN
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qlat(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 137
- generic_name :
- qlat
- long_name :
- LATENT HEAT FLUX
- name :
- QL
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 210
- generic_name :
- qtot
- long_name :
- TOTAL HEAT FLUX
- name :
- QT
- units :
- W/M**2
- standard_name :
- surface_downward_heat_flux_in_sea_water
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qsen(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 138
- generic_name :
- qsen
- long_name :
- SENSIBLE HEAT FLUX
- name :
- QS
- units :
- W m-2
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - swnet(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1495
- generic_name :
- sw
- long_name :
- NET SHORTWAVE RADIATION
- name :
- SWN
- units :
- W/M**2
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tau(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray - taux(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - tauy(time)float64dask.array<chunksize=(157800,), meta=np.ndarray>
- standard_name :
- surface_downward_y_stress
Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - theta(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - u(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - v(time, depth)float32dask.array<chunksize=(98066, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - wind_dir(time)float32dask.array<chunksize=(157800,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 410
- generic_name :
- long_name :
- WIND DIRECTION
- name :
- WD
- standard_name :
- wind_from_direction
- units :
- degrees
Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - pressure(depth)float64301.9 296.8 291.8 ... 5.028 -0.0
- standard_name :
- sea_water_pressure
- units :
- dbar
array([301.87732362, 296.84242473, 291.80764803, 286.77299352, 281.73846121, 276.70405112, 271.66976325, 266.63559761, 261.60155422, 256.56763308, 251.5338342 , 246.5001576 , 241.46660329, 236.43317126, 231.39986155, 226.36667414, 221.33360906, 216.30066632, 211.26784592, 206.23514788, 201.2025722 , 196.17011889, 191.13778797, 186.10557945, 181.07349333, 176.04152963, 171.00968835, 165.97796951, 160.94637311, 155.91489917, 150.8835477 , 145.8523187 , 140.82121218, 135.79022817, 130.75936665, 125.72862766, 120.69801119, 115.66751726, 110.63714587, 105.60689704, 100.57677078, 95.54676709, 90.51688599, 85.48712749, 80.4574916 , 75.42797832, 70.39858766, 65.36931965, 60.34017428, 55.31115157, 50.28225153, 45.25347416, 40.22481948, 35.1962875 , 30.16787822, 25.13959167, 20.11142784, 15.08338675, 10.0554684 , 5.02767282, -0. ]) - SA(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- standard_name :
- sea_water_absolute_salinity
- units :
- g/kg
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 6 graph layers Data type float64 numpy.ndarray - CT(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- standard_name :
- sea_water_conservative_temperature
- units :
- degC
- reference_scale :
- ITS-90
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 10 graph layers Data type float64 numpy.ndarray - α(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- units :
- 1/K
- standard_name :
- sea_water_thermal_expansion_coefficient
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - β(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- units :
- kg/g
- standard_name :
- sea_water_haline_contraction_coefficient
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - Tz(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- ℃/m
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Sz(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- g/kg/m
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - chi(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - KT(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - eps(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Jq(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - shred2(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Rig(time, depth)float64dask.array<chunksize=(98066, 61), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - sst(time)float64dask.array<chunksize=(98066,), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- units :
- degC
Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 5 graph layers Data type float64 numpy.ndarray - Tflx_dia_diff(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Rif(time, depthchi)float64dask.array<chunksize=(98066, 6), meta=np.ndarray>
- standard_name :
- flux_richardson_number
Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 53 graph layers Data type float64 numpy.ndarray - Jb(time, depthchi)float64dask.array<chunksize=(98066, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 50 graph layers Data type float64 numpy.ndarray
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (time: 157800, depth: 61, depthchi: 6) Coordinates: (12/19) deepest (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray> * depth (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0 eucmax (time) float64 dask.array<chunksize=(98066,), meta=np.ndarray> latitude float32 0.0 longitude float32 -140.0 mld (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray> ... ... oni (time) float32 -1.66 -1.66 -1.66 ... -0.87 -0.87 -0.87 en_mask (time) bool False False False ... False False False ln_mask (time) bool True True True True ... True True True True warm_mask (time) bool False False False False ... True True True cool_mask (time) bool True True True True ... False False False enso_transition (time) <U12 'La-Nina cool' ... 'La-Nina warm' Data variables: (12/38) N2 (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> N2T (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> Ri (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> Rig_T (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> S (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> S2 (time, depth) float32 dask.array<chunksize=(98066, 61), meta=np.ndarray> ... ... shred2 (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> Rig (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray> sst (time) float64 dask.array<chunksize=(98066,), meta=np.ndarray> Tflx_dia_diff (time, depthchi) float64 nan nan nan nan ... nan nan nan Rif (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray> Jb (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray> Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- time: 149184
- zi: 27
- zl: 27
- nv: 2
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]2000-01-01T00:30:00 ... 2017-12-...
array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000', '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(1080,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(1080,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 1080), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 3.84 MiB 230.98 kiB Shape (27, 149184) (27, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32-1.318 -1.318 ... -0.04526 -0.04526
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([-1.3176155 , -1.3176155 , -1.3176155 , ..., -0.04525503, -0.04525503, -0.04525503], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., '____________', '____________', '____________'], dtype='<U12')
- KPP_OBLdepth(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- standard_name :
- ocean_vertical_y_viscosity
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
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- long_name :
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Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
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Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_methods :
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- units :
- m s-1
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
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- units :
- m
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(1080, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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- units :
- Cm$^{-1}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(149184,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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- standard_name :
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Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(1080, 27), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 30.73 MiB 1.80 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 7 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(1080, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
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- units :
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Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(1080,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
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- units :
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Array Chunk Bytes 582.75 kiB 34.22 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 149184, zi: 27, zl: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017-12-3... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 -1.318 -1.318 -1.318 ... -0.04526 -0.04526 en_mask (time) bool False False False False ... False False False ln_mask (time) bool True True True True ... False False False warm_mask (time) bool False False False False ... True True True cool_mask (time) bool True True True True ... False False False enso_transition (time) <U12 'La-Nina cool' ... '____________' Data variables: (12/38) KPP_OBLdepth (time) float32 dask.array<chunksize=(149184,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray> Kd_heat (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray> Kv_u (time, zl) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray> Kv_v (time, zl) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(1080, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(1080,), meta=np.ndarray> Attributes: title: baselinebaseline- time: 130968
- zi: 27
- zl: 27
- nv: 2
- nv(nv)float641.0 2.0
- cartesian_axis :
- N
- long_name :
- vertex number
- units :
- none
array([1., 2.])
- time(time)datetime64[ns]2003-01-07T00:30:00 ... 2017-12-...
array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000', '2003-01-07T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
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- X
- domain_decomposition :
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- long_name :
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- units :
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array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
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- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- cartesian_axis :
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- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
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array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 3.37 MiB 230.98 kiB Shape (27, 130968) (27, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan ... 0.09026 0.09026
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([ nan, nan, nan, ..., 0.09026337, 0.09026337, 0.09026337], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
- KPP_N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of vertical shear used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Lateral diffusion tracer content tendency for T
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Boundary forcing heat tendency
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
- surface_downward_y_stress
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- time_avg_info :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
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- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 26.98 MiB 1.80 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 511.59 kiB 34.22 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 130968, zi: 27, zl: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-07T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 ... -2.5 -0.0 ... ... oni (time) float32 nan nan ... 0.09026 0.09026 en_mask (time) bool False False ... False False ln_mask (time) bool False False ... False False warm_mask (time) bool True True True ... True True cool_mask (time) bool False False ... False False enso_transition (time) <U12 '____________' ... '_________... Data variables: (12/53) KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(130968,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5kpp.lmd.004- time: 157680
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2000-01-01T00:30:00 ... 2017-12-...
array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000', '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
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- long_name :
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array(-140.)
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- axis :
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- domain_decomposition :
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- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
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- domain_decomposition :
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- long_name :
- q point nominal latitude
- units :
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array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
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Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.06 MiB 230.98 kiB Shape (27, 157680) (27, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float32-1.277 -1.277 -1.277 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([-1.2772478, -1.2772478, -1.2772478, ..., nan, nan, nan], dtype=float32) - en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'La-Nina cool' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
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- units :
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Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
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- units :
- nondim
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
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- units :
- kg/m3
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
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- units :
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Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
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- long_name :
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- standard_name :
- surface_downward_y_stress
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
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- long_name :
- Sea Water Potential Temperature
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- units :
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Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water X Velocity
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- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
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- long_name :
- Ocean grid-cell volume
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_measures :
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- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
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- units :
- C-1
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
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- units :
- kg/g
Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
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- long_name :
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Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(157680,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
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Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 32.48 MiB 1.80 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 7 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
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- units :
- degC
Array Chunk Bytes 615.94 kiB 34.22 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (time: 157680, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 -1.277 -1.277 -1.277 ... nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool True True True True ... False False False warm_mask (time) bool False False False False ... True True True cool_mask (time) bool True True True True ... False False False enso_transition (time) <U12 'La-Nina cool' ... 'El-Nino warm' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KD=0, KV=0new_baseline.hb- time: 113880
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2015-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000', '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
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- domain_decomposition :
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- long_name :
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- units :
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array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
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- long_name :
- h point nominal latitude
- units :
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array(0.06249997)
- yq()float64-0.0625
- axis :
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- domain_decomposition :
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- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 2.93 MiB 230.98 kiB Shape (27, 113880) (27, 8760) Dask graph 13 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
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- units :
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Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(113880,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 23.46 MiB 1.80 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 444.84 kiB 34.22 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 113880, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2015-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan nan ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'El-Nino warm' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- time: 105120
- zl: 27
- zi: 27
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2014-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2014-12-31T21:30:00.000000000', '2014-12-31T22:30:00.000000000', '2014-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-230.8 -212.0 -194.4 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-240.8 -221.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(27, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 2.71 MiB 230.98 kiB Shape (27, 105120) (27, 8760) Dask graph 12 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float32nan nan nan nan ... nan nan nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- ONI
- standard_name :
- oceanic_nino_index
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... '____________'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
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- units :
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Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 27), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(105120,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 27), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 21.65 MiB 1.80 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 6 graph layers Data type float64 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 26), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 410.62 kiB 34.22 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 105120, zl: 27, zi: 27, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2014-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 nan nan nan nan nan ... nan nan nan nan en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... '____________' Data variables: (12/48) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray> ... ... Jq (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
ref = tree["TAO"].ds.reset_coords(drop=True).cf.sel(Z=slice(-120, None))
counts = np.minimum(ref["S2"].cf.count("Z"), ref["N2T"].cf.count("Z")).load()
def calc_histograms(ds):
ds = ds.copy()
ds["tao_mask"] = counts.reindex(time=ds.time, method="nearest") > 5
ds["tao_mask"].attrs = {
"description": "True when there are more than 5 5-m T, u, v in TAO dataset"
}
# ds = ds.where(ds.tao_mask)
return ds.update(mixpods.pdf_N2S2(ds))
tree = tree.map_over_subtree(calc_histograms)
daily composite#
%autoreload
dailies = tree.map_over_subtree(mixpods.daily_composites)
dailies
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- reference_pressure()int640
array(0)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float649.959e-06 1.107e-05 ... 0.0001646
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[9.95869314e-06, 1.10713814e-05, 1.81385305e-05], [7.62679606e-06, 9.84649659e-06, 1.98903482e-05], [8.26769194e-06, 1.03487366e-05, 1.69229065e-05], [5.09341517e-06, 1.06415264e-05, 9.94711354e-06], [6.27289392e-06, 1.01286448e-05, 9.09556433e-06], [5.85044689e-06, 9.09517149e-06, 1.06714061e-05], [5.92212630e-06, 8.40077865e-06, 1.11052356e-05], [6.47377658e-06, 9.78053302e-06, 1.13559910e-05], [5.77007820e-06, 1.00437481e-05, 1.54385405e-05], [5.60030739e-06, 8.32575368e-06, 1.31285365e-05], [5.27227053e-06, 7.15577324e-06, 1.44267402e-05], [5.03200713e-06, 9.93609875e-06, 1.68541630e-05], [6.42531180e-06, 1.04701955e-05, 1.45938509e-05], [5.45290240e-06, 1.06380336e-05, 1.94790247e-05], [5.05695470e-06, 1.08870254e-05, 2.13555496e-05], [6.78671892e-06, 1.23528905e-05, 2.01087241e-05], [9.80084606e-06, 1.38124457e-05, 2.85315836e-05], [6.90458131e-06, 1.42042099e-05, 2.59700678e-05], [8.44895131e-06, 1.38211283e-05, 2.92244979e-05], [6.96328801e-06, 1.80749482e-05, 2.81233184e-05], ... [2.49796366e-05, 1.51222154e-04, 1.54133302e-03], [2.54375198e-05, 1.92556637e-04, 1.98323078e-03], [2.95016029e-05, 2.74405783e-04, 2.23706350e-03], [2.91381387e-05, 5.29042782e-04, 2.46231496e-03], [3.22679193e-05, 6.25229486e-04, 2.36063586e-03], [4.87863828e-05, 6.82247064e-04, 2.44611497e-03], [7.65505437e-05, 8.72437582e-04, 2.08043411e-03], [9.60910794e-05, 7.32102430e-04, 2.33006858e-03], [8.45055247e-05, 8.06508643e-04, 1.81524742e-03], [9.68881747e-05, 7.19295971e-04, 2.07390795e-03], [1.12205990e-04, 8.31837440e-04, 1.82439093e-03], [1.25092879e-04, 8.00568096e-04, 1.85697065e-03], [1.41021393e-04, 7.84944210e-04, 1.71427588e-03], [1.35184262e-04, 7.04609354e-04, 1.41404584e-03], [1.12211968e-04, 3.97215676e-04, 1.07973119e-03], [8.04180511e-05, 2.89701497e-04, 6.65654642e-04], [6.64714222e-05, 1.94172831e-04, 4.08622947e-04], [5.75134301e-05, 1.40309406e-04, 2.67942773e-04], [5.13853203e-05, 1.01548040e-04, 1.64550217e-04], [4.10933487e-05, 8.66966898e-05, 1.64556856e-04]]]) - eps(depth, hour, tau_bins)float644.205e-09 6.427e-09 ... 1.608e-08
array([[[4.20484047e-09, 6.42665336e-09, 1.03495228e-08], [3.39942155e-09, 6.09951608e-09, 9.95406422e-09], [3.22205779e-09, 6.00608012e-09, 9.89785322e-09], [2.43384740e-09, 5.65188162e-09, 6.43540665e-09], [2.34056719e-09, 4.68248253e-09, 5.86884639e-09], [2.42516755e-09, 4.36594516e-09, 6.04769734e-09], [2.01203910e-09, 4.50894201e-09, 6.80387559e-09], [2.77474401e-09, 4.06819005e-09, 5.78686391e-09], [3.02695691e-09, 4.19429197e-09, 9.25259960e-09], [2.16803642e-09, 4.07562895e-09, 7.99217582e-09], [2.25024698e-09, 3.86947366e-09, 1.08315576e-08], [2.34519609e-09, 4.58983072e-09, 8.80408647e-09], [2.43251603e-09, 5.04441870e-09, 1.00646850e-08], [2.87316856e-09, 4.50655083e-09, 1.06291960e-08], [3.21955035e-09, 5.40452670e-09, 1.13614463e-08], [2.93556264e-09, 5.97819732e-09, 1.35444442e-08], [3.32274328e-09, 6.36330330e-09, 1.69035479e-08], [3.41472292e-09, 7.45018817e-09, 1.40619889e-08], [4.16563716e-09, 6.50261284e-09, 1.49772749e-08], [3.93711329e-09, 8.03877507e-09, 1.71921395e-08], ... [4.32062308e-09, 2.14336097e-08, 1.84017958e-07], [4.32244969e-09, 3.23795526e-08, 1.77984766e-07], [4.99443371e-09, 4.76849714e-08, 1.82287235e-07], [6.67475746e-09, 7.43192793e-08, 1.80947783e-07], [7.69190689e-09, 8.48603912e-08, 1.83042578e-07], [1.24068720e-08, 8.70767941e-08, 1.63145058e-07], [1.75400970e-08, 9.81732289e-08, 1.55381279e-07], [2.07108437e-08, 8.34432489e-08, 1.47163501e-07], [2.09570176e-08, 9.55806383e-08, 1.28104332e-07], [2.49357959e-08, 7.48081255e-08, 1.38691872e-07], [2.02488017e-08, 7.54215709e-08, 1.17639124e-07], [2.72920187e-08, 7.42685031e-08, 1.22579504e-07], [2.84401490e-08, 7.54687544e-08, 1.20862216e-07], [2.32893736e-08, 6.57975258e-08, 9.00575173e-08], [1.80033299e-08, 4.22211119e-08, 6.23893426e-08], [1.57912073e-08, 2.80175873e-08, 4.02353834e-08], [1.23950854e-08, 2.20880606e-08, 2.95446015e-08], [1.11499973e-08, 1.59336048e-08, 1.98472383e-08], [8.36356549e-09, 1.27149248e-08, 1.63302630e-08], [7.16264221e-09, 1.10760452e-08, 1.60790152e-08]]]) - chi(depth, hour, tau_bins)float641.67e-08 3.739e-08 ... 1.95e-08
array([[[1.67021892e-08, 3.73895360e-08, 7.93282652e-08], [1.45695729e-08, 3.21540306e-08, 7.51363908e-08], [1.66509708e-08, 3.17736964e-08, 7.22932589e-08], [1.00107661e-08, 3.13534986e-08, 4.77883670e-08], [1.33179056e-08, 2.40084728e-08, 3.57552816e-08], [9.22712244e-09, 2.50863744e-08, 4.33211842e-08], [8.08744573e-09, 2.38605289e-08, 3.82013297e-08], [1.09612925e-08, 2.27882209e-08, 3.99283958e-08], [1.34920747e-08, 1.97982703e-08, 4.72313840e-08], [9.85650711e-09, 2.04048889e-08, 5.14985775e-08], [1.08937936e-08, 2.35725432e-08, 6.87134261e-08], [1.21212706e-08, 2.51579652e-08, 7.15330504e-08], [1.32546719e-08, 3.39587720e-08, 9.47722781e-08], [1.69953873e-08, 2.60973815e-08, 7.29116184e-08], [1.67531863e-08, 3.07021553e-08, 6.79356643e-08], [1.77120188e-08, 4.09737308e-08, 7.29900722e-08], [1.93716942e-08, 4.31379369e-08, 9.26064400e-08], [2.09236218e-08, 4.34670007e-08, 9.19779817e-08], [2.48015149e-08, 3.95476993e-08, 8.28554984e-08], [2.05587984e-08, 5.27063058e-08, 8.39105570e-08], ... [6.58562963e-09, 4.00100193e-08, 1.82930124e-07], [8.27051334e-09, 5.94085776e-08, 1.62815748e-07], [1.05461194e-08, 7.08934926e-08, 1.68249950e-07], [1.42336592e-08, 9.11751999e-08, 1.81170217e-07], [1.72444375e-08, 9.91038146e-08, 1.50109762e-07], [2.48941347e-08, 1.02137827e-07, 1.58365512e-07], [3.49353545e-08, 1.15828778e-07, 1.27671006e-07], [4.11614687e-08, 9.85426121e-08, 1.36028494e-07], [3.54397051e-08, 9.90287508e-08, 1.15470101e-07], [4.15427310e-08, 8.94060867e-08, 1.12016269e-07], [3.92166332e-08, 8.64900353e-08, 9.95322983e-08], [5.45326891e-08, 8.52935771e-08, 1.10918666e-07], [4.42275054e-08, 7.57406371e-08, 9.45690680e-08], [4.10326472e-08, 7.06337841e-08, 7.52282802e-08], [3.53795830e-08, 4.58133432e-08, 5.44219219e-08], [2.37814091e-08, 3.15729107e-08, 3.78253266e-08], [1.95929775e-08, 2.64875960e-08, 3.30521637e-08], [1.66729435e-08, 2.15147840e-08, 2.22266528e-08], [1.50428825e-08, 1.53505017e-08, 1.73559861e-08], [1.08272541e-08, 1.40830926e-08, 1.94961160e-08]]]) - Jb(depth, hour, tau_bins)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], ... [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan]]]) - Jq(depth, hour, tau_bins)float64-1.003 -1.842 ... -2.971 -4.428
array([[[ -1.00256446, -1.84244745, -2.80850936], [ -0.91887084, -1.62464068, -2.56282757], [ -0.88374135, -1.58679603, -2.88823456], [ -0.70443327, -1.6103817 , -1.77298658], [ -0.68665724, -1.27295597, -1.77228 ], [ -0.66664796, -1.19898684, -1.67626741], [ -0.59730391, -1.23465212, -1.70404798], [ -0.7235757 , -1.18544042, -1.59040206], [ -0.75019594, -1.04231212, -2.57469427], [ -0.57483421, -1.14463015, -2.24184293], [ -0.63752255, -1.10632234, -2.90389878], [ -0.65130446, -1.26093457, -2.35183439], [ -0.66722366, -1.3861301 , -2.72893158], [ -0.81851688, -1.24810229, -2.85460253], [ -0.72343038, -1.27913762, -2.93287239], [ -0.82057933, -1.73613097, -3.35338815], [ -0.92375014, -1.80837082, -4.44104197], [ -0.87323403, -2.01851581, -3.77664889], [ -1.19708228, -1.7267686 , -4.23530912], [ -0.98020427, -2.19262619, -4.13656709], ... [ -1.11297409, -5.84520527, -47.88722398], [ -1.20329183, -9.40240756, -45.37124552], [ -1.33200184, -12.95762863, -46.64407742], [ -1.73752661, -19.31055455, -45.85240017], [ -2.00999545, -20.92691833, -45.36418532], [ -3.38542206, -21.6217159 , -41.41348273], [ -4.660082 , -25.72382479, -37.29098972], [ -5.53276281, -20.1004968 , -36.00067158], [ -5.56787429, -22.40393839, -31.23276616], [ -5.82381354, -19.36849336, -34.99038496], [ -5.01723603, -19.13635109, -28.69033267], [ -6.73045836, -19.73596063, -28.18919034], [ -7.09852648, -18.76319064, -30.2266072 ], [ -6.12788 , -16.48818028, -21.37988398], [ -4.62785644, -10.76205284, -16.19281641], [ -4.21420556, -7.65540787, -10.55800383], [ -3.29425731, -5.96463463, -7.87434395], [ -2.86694385, -4.28650835, -5.42953264], [ -2.33320233, -3.38921404, -4.47756404], [ -1.82422195, -2.97098078, -4.42772743]]]) - S2(depth, hour, tau_bins)float320.0001345 0.0002001 ... 0.0001682
- long_name :
- $S^2$
array([[[0.00013448, 0.00020006, 0.00030025], [0.00013459, 0.00019925, 0.00030133], [0.00013651, 0.00019142, 0.0003029 ], [0.00012919, 0.00020452, 0.00029948], [0.00013896, 0.0002124 , 0.00030397], [0.00015213, 0.00019731, 0.00031407], [0.00014794, 0.00019219, 0.00030202], [0.00015217, 0.00019176, 0.00030711], [0.0001403 , 0.00019476, 0.00029934], [0.00013978, 0.00018397, 0.00030806], [0.00014281, 0.00018728, 0.00030986], [0.00014091, 0.00018794, 0.00031204], [0.00013823, 0.0001874 , 0.000305 ], [0.00013669, 0.00019039, 0.00031266], [0.00013547, 0.00019014, 0.00030768], [0.0001324 , 0.00018962, 0.00029873], [0.00013033, 0.00018335, 0.00029489], [0.00013093, 0.0001916 , 0.00030148], [0.00014072, 0.00018797, 0.00031962], [0.00014378, 0.00018233, 0.00031395], ... [0.00030681, 0.00022737, 0.00020181], [0.00031573, 0.00023622, 0.000205 ], [0.00032313, 0.0002395 , 0.00019791], [0.00032533, 0.00024731, 0.00017745], [0.0003447 , 0.00024576, 0.00016273], [0.00036876, 0.0002621 , 0.00016414], [0.0003664 , 0.000238 , 0.00016017], [0.00036569, 0.00023603, 0.00015653], [0.00035129, 0.00023055, 0.00014936], [0.00035962, 0.00021455, 0.00015314], [0.00033461, 0.00022228, 0.00015203], [0.00038251, 0.00024297, 0.00017307], [0.00040409, 0.000252 , 0.0001731 ], [0.00037549, 0.00023915, 0.00015291], [0.00036383, 0.00021962, 0.0001535 ], [0.00036171, 0.00020565, 0.0001415 ], [0.0003339 , 0.00020438, 0.00015409], [0.00035724, 0.00019812, 0.00015214], [0.0003461 , 0.00020423, 0.00015108], [0.0003532 , 0.00020651, 0.00016817]]], dtype=float32) - N2(depth, hour, tau_bins)float640.0002082 0.0002229 ... 2.895e-05
- long_name :
- $N^2$
array([[[2.08151738e-04, 2.22862104e-04, 2.14441416e-04], [2.16603991e-04, 2.24483495e-04, 2.21728203e-04], [2.15355923e-04, 2.21838325e-04, 2.18587475e-04], [2.15544742e-04, 2.22885541e-04, 2.12326524e-04], [2.16644011e-04, 2.13538050e-04, 2.23611350e-04], [2.09853593e-04, 2.20879385e-04, 2.19642997e-04], [1.98066216e-04, 2.21105441e-04, 2.18486263e-04], [2.16155790e-04, 2.18854377e-04, 2.23897831e-04], [2.21096656e-04, 2.18472571e-04, 2.21506161e-04], [2.16910676e-04, 2.20828730e-04, 2.15561007e-04], [2.20007830e-04, 2.25418650e-04, 2.14505446e-04], [2.13170636e-04, 2.18209400e-04, 2.26985607e-04], [2.08379942e-04, 2.20359529e-04, 2.28006372e-04], [2.18869956e-04, 2.12548021e-04, 2.37007497e-04], [2.22657337e-04, 2.18700017e-04, 2.19859678e-04], [2.16105074e-04, 2.18497812e-04, 2.29437082e-04], [2.15577658e-04, 2.11075774e-04, 2.20386303e-04], [2.14909841e-04, 2.19245199e-04, 2.10944560e-04], [2.18744613e-04, 2.23280408e-04, 2.02637232e-04], [2.18981413e-04, 2.22255352e-04, 2.07395227e-04], ... [6.96024719e-05, 4.77216368e-05, 3.52536021e-05], [7.21013450e-05, 4.76598289e-05, 3.29697584e-05], [7.22574894e-05, 5.01282745e-05, 3.12980790e-05], [7.68367868e-05, 4.81516730e-05, 3.00344601e-05], [7.68827915e-05, 4.78022934e-05, 2.65751683e-05], [7.66054714e-05, 4.80892891e-05, 2.51018158e-05], [7.63678736e-05, 4.39940175e-05, 2.65891577e-05], [7.64221137e-05, 4.27375694e-05, 2.65801164e-05], [7.63855405e-05, 4.13222946e-05, 2.54512380e-05], [7.23437008e-05, 3.95318950e-05, 2.40070337e-05], [7.32406023e-05, 3.87474723e-05, 2.17285776e-05], [7.84975393e-05, 3.86002397e-05, 2.22512416e-05], [7.96900786e-05, 3.91479868e-05, 2.12247197e-05], [7.60129546e-05, 3.42026320e-05, 2.17803309e-05], [7.44090646e-05, 3.69594533e-05, 2.22136917e-05], [7.49782129e-05, 3.66955626e-05, 2.24673463e-05], [7.37285223e-05, 3.66268637e-05, 2.49871805e-05], [7.33997174e-05, 3.74016674e-05, 2.55136179e-05], [7.08907159e-05, 4.04018172e-05, 2.61858948e-05], [6.88821220e-05, 4.19587334e-05, 2.89518715e-05]]]) - Rig(depth, hour, tau_bins)float641.406 1.252 ... 0.07694 0.05421
- long_name :
- $Ri^g$
array([[[1.40644738, 1.25178115, 0.74488556], [1.41762533, 1.35060308, 0.76782803], [1.47274045, 1.28796047, 0.71477707], [1.77206894, 1.23581944, 0.85990554], [1.42856916, 1.18887077, 0.86747588], [1.37627981, 1.250239 , 0.79682319], [1.35225077, 1.14314482, 0.89240256], [1.53488295, 1.13362321, 0.81762861], [1.43149522, 1.18854333, 0.82108334], [1.69124541, 1.31780546, 0.74364754], [1.63997589, 1.2650186 , 0.75719731], [1.60196071, 1.29321876, 0.80676174], [1.76211032, 1.29848346, 0.82638659], [1.89364181, 1.2401134 , 0.81697784], [1.75861073, 1.2281106 , 0.82555718], [1.79334254, 1.25421119, 0.87983679], [1.75460255, 1.23742487, 0.81284393], [1.6443649 , 1.20305246, 0.80500277], [1.90235508, 1.39715558, 0.75862198], [1.59556172, 1.27929263, 0.69249317], ... [0.11922037, 0.08834196, 0.05390135], [0.11364714, 0.0985151 , 0.04969669], [0.11955433, 0.09013164, 0.04259646], [0.13152116, 0.08148522, 0.04038412], [0.12462708, 0.07752266, 0.0412092 ], [0.1059685 , 0.08015321, 0.03483511], [0.11336615, 0.07559725, 0.03430897], [0.10954652, 0.07044978, 0.03097055], [0.10013768, 0.07146289, 0.03221148], [0.08570449, 0.07030557, 0.0349513 ], [0.09017043, 0.06546468, 0.03168679], [0.08669634, 0.0582093 , 0.03019886], [0.09715547, 0.06009826, 0.02640776], [0.07935822, 0.05776852, 0.03262731], [0.09296572, 0.05099493, 0.03820643], [0.08410832, 0.06245423, 0.04184208], [0.10067066, 0.0679023 , 0.04833413], [0.10772209, 0.06960988, 0.05154636], [0.096249 , 0.07780057, 0.05655469], [0.10424006, 0.07694161, 0.05421351]]]) - Rig_T(depth, hour, tau_bins)float641.901 1.204 0.6325 ... 0.12 0.1185
- long_name :
- $Ri^g_T$
array([[[1.90144926, 1.20402004, 0.63250394], [1.97722436, 1.16803555, 0.63929729], [1.82363029, 1.19271362, 0.63758984], [2.00858352, 1.17413288, 0.64556353], [1.764249 , 1.08857607, 0.63981687], [1.6722678 , 1.14029434, 0.63841363], [1.64416596, 1.07404686, 0.69016643], [1.80985361, 1.1286188 , 0.66627749], [1.84750455, 1.11619713, 0.65720696], [1.85832509, 1.14625708, 0.64646297], [1.82383741, 1.28825209, 0.60547409], [1.90534517, 1.22815954, 0.65631865], [1.92757436, 1.17589125, 0.6729475 ], [1.95646798, 1.15944217, 0.67811545], [2.15228629, 1.2346199 , 0.68925813], [2.17114092, 1.25440367, 0.67767255], [1.9283005 , 1.25106341, 0.65996391], [2.09583235, 1.16882993, 0.64695664], [2.00001933, 1.18052973, 0.63612982], [1.95096458, 1.24909609, 0.60900943], ... [0.14035465, 0.15645461, 0.13741426], [0.15093877, 0.1540195 , 0.12269896], [0.1517013 , 0.14701272, 0.10974087], [0.15332836, 0.13742055, 0.10176559], [0.14785674, 0.1249745 , 0.09094855], [0.14468148, 0.11747535, 0.08905629], [0.1371656 , 0.11502819, 0.08797114], [0.13831776, 0.10755145, 0.08060554], [0.1255792 , 0.1066724 , 0.07354782], [0.11734482, 0.09774099, 0.07285993], [0.12316585, 0.0914999 , 0.07129744], [0.11004655, 0.0806289 , 0.05700402], [0.12113939, 0.07903602, 0.04899825], [0.11156332, 0.08470378, 0.06233322], [0.11291802, 0.09061568, 0.07497081], [0.12169541, 0.09945144, 0.08995757], [0.12816946, 0.11179433, 0.0962672 ], [0.13384635, 0.11637368, 0.11296923], [0.12542545, 0.12154479, 0.11954271], [0.12356876, 0.12001706, 0.11848762]]]) - tau(hour, tau_bins)float640.02709 0.05733 ... 0.05745 0.09308
array([[0.0270877 , 0.05732647, 0.09203958], [0.02680315, 0.05759896, 0.09214987], [0.0266931 , 0.05764483, 0.09244683], [0.02699333, 0.05713952, 0.09263131], [0.02688743, 0.0573335 , 0.09261309], [0.02682686, 0.05738899, 0.0923755 ], [0.02736078, 0.05730989, 0.09163058], [0.02709459, 0.05719675, 0.09233963], [0.0260143 , 0.05757715, 0.09312405], [0.02643325, 0.05748537, 0.09276128], [0.02604512, 0.05695688, 0.09281864], [0.02653221, 0.05750187, 0.09254788], [0.02541264, 0.05698433, 0.09270642], [0.0258142 , 0.0573662 , 0.09281572], [0.02577848, 0.05747551, 0.09283653], [0.02582663, 0.05766199, 0.09305366], [0.0262083 , 0.05818581, 0.09442765], [0.02586172, 0.05809102, 0.0939246 ], [0.02600226, 0.05773976, 0.09318213], [0.02695185, 0.05763241, 0.09303027], [0.02669914, 0.05756156, 0.09276316], [0.02679048, 0.05723235, 0.09331579], [0.02705673, 0.05729915, 0.09280436], [0.02745168, 0.05745369, 0.09308222]])
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 latitude float32 0.0 longitude float32 -140.0 reference_pressure int64 0 * hour (hour) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float64 9.959e-06 ... 0.0001646 eps (depth, hour, tau_bins) float64 4.205e-09 ... 1.608e-08 chi (depth, hour, tau_bins) float64 1.67e-08 ... 1.95e-08 Jb (depth, hour, tau_bins) float64 nan nan nan ... nan nan Jq (depth, hour, tau_bins) float64 -1.003 -1.842 ... -4.428 S2 (depth, hour, tau_bins) float32 0.0001345 ... 0.0001682 N2 (depth, hour, tau_bins) float64 0.0002082 ... 2.895e-05 Rig (depth, hour, tau_bins) float64 1.406 1.252 ... 0.05421 Rig_T (depth, hour, tau_bins) float64 1.901 1.204 ... 0.1185 tau (hour, tau_bins) float64 0.02709 0.05733 ... 0.09308 Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006551
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[1.00062630e-06, 1.00062675e-06, 1.00062948e-06], [1.00062630e-06, 1.00062675e-06, 1.00062914e-06], [1.00062630e-06, 1.00062664e-06, 1.00070815e-06], [1.00062630e-06, 1.00062675e-06, 1.00062823e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062812e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00062869e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00570742e-06], [1.00062630e-06, 1.00062664e-06, 1.00063801e-06], [1.00062630e-06, 1.00062664e-06, 1.00505042e-06], [1.00062630e-06, 1.00062664e-06, 1.00063016e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062653e-06, 1.00062778e-06], [1.00062630e-06, 1.00062664e-06, 1.00062721e-06], [1.00062630e-06, 1.00062653e-06, 1.00062732e-06], [1.00062630e-06, 1.00062653e-06, 1.00062744e-06], [1.00062618e-06, 1.00062664e-06, 1.00062789e-06], ... [1.31017505e-03, 1.84910290e-03, 3.04656965e-03], [1.20016653e-03, 1.93014904e-03, 2.43997667e-02], [1.26303639e-03, 2.55707884e-03, 3.35960016e-02], [1.21755805e-03, 3.45059624e-03, 4.70925234e-02], [1.20411417e-03, 6.63228473e-03, 5.48477545e-02], [1.20652630e-03, 1.19018322e-02, 6.12227395e-02], [1.33580097e-03, 1.68863572e-02, 6.64590597e-02], [1.38179970e-03, 2.08421741e-02, 7.17242733e-02], [1.61000830e-03, 2.49606222e-02, 7.40951598e-02], [1.71344716e-03, 2.64077000e-02, 7.43862391e-02], [1.82845863e-03, 2.77032442e-02, 7.27289319e-02], [1.99175603e-03, 2.91718896e-02, 7.13082328e-02], [1.64016616e-03, 1.40310498e-02, 5.11850938e-02], [2.00258894e-03, 2.12116283e-03, 1.37765761e-02], [1.67123857e-03, 1.55570242e-03, 4.48137894e-03], [1.43854087e-03, 6.79666526e-04, 3.39539582e-03], [2.04196898e-03, 1.61034847e-03, 7.48192775e-04], [2.00651051e-03, 1.96600612e-03, 5.42554015e-04], [1.92917790e-03, 1.86887372e-03, 4.04946972e-04], [1.80337648e-03, 1.76201062e-03, 6.55124430e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float322.922e-08 1.368e-07 ... 2.348e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[2.92183362e-08, 1.36777288e-07, 2.27541989e-07], [2.90522504e-08, 1.39135309e-07, 2.38471500e-07], [3.01237151e-08, 1.37680928e-07, 2.37547908e-07], [3.13189972e-08, 1.36566356e-07, 2.33948427e-07], [3.05815284e-08, 1.31672735e-07, 2.26799813e-07], [2.99091028e-08, 1.30052570e-07, 2.21252151e-07], [2.86628925e-08, 1.24942943e-07, 2.08034521e-07], [2.91027824e-08, 1.22521584e-07, 2.01086578e-07], [3.04016972e-08, 1.19735688e-07, 1.95425770e-07], [3.16648112e-08, 1.19069099e-07, 1.88375097e-07], [3.19412052e-08, 1.16080244e-07, 1.91520044e-07], [3.19991642e-08, 1.15435903e-07, 1.95864132e-07], [3.21174944e-08, 1.14669440e-07, 1.85658010e-07], [3.06349754e-08, 1.09434126e-07, 1.88835344e-07], [2.94250633e-08, 1.05647260e-07, 1.79058816e-07], [2.91206543e-08, 1.01917209e-07, 1.72599258e-07], [2.79490671e-08, 1.02308121e-07, 1.71204775e-07], [2.62092996e-08, 1.02718658e-07, 1.88728990e-07], [2.34818103e-08, 1.03739211e-07, 1.98115629e-07], [2.53572559e-08, 1.08082645e-07, 2.19582347e-07], ... [1.05008013e-07, 1.26518316e-07, 2.35757994e-07], [1.06341147e-07, 3.14839610e-07, 4.57502239e-07], [1.06996765e-07, 3.11565771e-07, 3.97820173e-07], [1.10653318e-07, 4.02599085e-07, 3.34023014e-07], [1.23020456e-07, 3.97435031e-07, 2.64594632e-07], [1.38877965e-07, 3.69533637e-07, 2.31153578e-07], [1.55802979e-07, 3.41629004e-07, 1.98067397e-07], [1.71049919e-07, 2.96018698e-07, 1.74979576e-07], [1.85744952e-07, 2.51837662e-07, 1.61111473e-07], [1.85082229e-07, 2.25853171e-07, 1.47434548e-07], [1.84550530e-07, 2.08649141e-07, 1.40715855e-07], [1.84773924e-07, 1.95753216e-07, 1.38844769e-07], [1.71781878e-07, 1.28045869e-07, 9.42439726e-08], [2.01993771e-07, 7.92810866e-08, 3.86436980e-08], [1.56741407e-07, 1.01999774e-07, 2.64388085e-08], [1.30592014e-07, 6.58632686e-08, 2.03692281e-08], [1.72341856e-07, 9.93372993e-08, 1.35294016e-08], [1.64754084e-07, 1.01589876e-07, 1.64205378e-08], [1.52421507e-07, 9.34200841e-08, 1.83120470e-08], [1.39953386e-07, 8.80785507e-08, 2.34809825e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float323.153e-08 5.147e-08 ... 5.686e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[3.15311368e-08, 5.14684650e-08, 9.00696406e-08], [3.15410986e-08, 5.30324087e-08, 9.01792561e-08], [3.15075219e-08, 5.37205800e-08, 9.82160913e-08], [3.20624061e-08, 5.38295062e-08, 9.03460275e-08], [3.18296536e-08, 5.18737444e-08, 9.16030842e-08], [3.06467669e-08, 5.12408036e-08, 9.39065075e-08], [3.04877297e-08, 4.99954069e-08, 8.79619648e-08], [3.04382226e-08, 4.94467329e-08, 9.59539150e-08], [3.07606456e-08, 4.88297900e-08, 9.66301599e-08], [3.07989403e-08, 4.88755845e-08, 9.55644310e-08], [3.07602761e-08, 4.79528133e-08, 9.98426657e-08], [3.08518295e-08, 4.82226348e-08, 9.68349596e-08], [3.17893125e-08, 4.86282445e-08, 9.36632176e-08], [3.16449871e-08, 4.65035299e-08, 9.43617451e-08], [3.08822727e-08, 4.60562504e-08, 9.55800061e-08], [3.11754427e-08, 4.47915767e-08, 8.63022080e-08], [3.06896979e-08, 4.50557103e-08, 8.33114768e-08], [3.02294971e-08, 4.45752235e-08, 8.25163937e-08], [2.99401677e-08, 4.39887842e-08, 8.17240959e-08], [3.01301455e-08, 4.42664927e-08, 8.81232864e-08], ... [4.69678980e-08, 7.31207379e-08, 1.80065271e-07], [4.44437944e-08, 1.60526312e-07, 5.26232157e-07], [4.59130831e-08, 2.16911602e-07, 4.20312915e-07], [4.35025953e-08, 2.93830965e-07, 3.34603868e-07], [4.81800519e-08, 2.92471725e-07, 2.25865008e-07], [5.43300516e-08, 2.66074068e-07, 1.74853156e-07], [6.02832841e-08, 2.26358566e-07, 1.33450698e-07], [6.76159573e-08, 1.83526524e-07, 1.02248393e-07], [7.15624537e-08, 1.46292123e-07, 8.52350865e-08], [6.93415956e-08, 1.20850444e-07, 7.18475022e-08], [6.45878373e-08, 1.03103801e-07, 6.89958028e-08], [6.24963974e-08, 8.90549074e-08, 6.63747244e-08], [3.71018416e-08, 3.94645703e-08, 4.55000446e-08], [6.99339324e-08, 8.64094130e-09, 1.39391068e-08], [5.24933412e-08, 9.25000787e-09, 4.61419702e-09], [5.24419441e-08, 6.12304518e-09, 5.45677326e-09], [1.02914335e-07, 2.33580533e-08, 2.31011832e-09], [1.05326329e-07, 3.94027495e-08, 2.59428723e-09], [9.97600011e-08, 4.49156161e-08, 3.48193008e-09], [8.96089247e-08, 4.73158046e-08, 5.68561020e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-3.583e-10 -4.566e-10 ... -4.18e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[-3.58277630e-10, -4.56598898e-10, -6.36808883e-10], [-3.61272345e-10, -4.61960303e-10, -6.18004647e-10], [-3.62497338e-10, -4.62224758e-10, -6.27580043e-10], [-3.65806746e-10, -4.59576710e-10, -6.01129591e-10], [-3.65918185e-10, -4.55617932e-10, -6.07656758e-10], [-3.62261526e-10, -4.51856774e-10, -6.12711770e-10], [-3.61771529e-10, -4.47667459e-10, -6.07582207e-10], [-3.55180552e-10, -4.47039850e-10, -6.31304398e-10], [-3.58695351e-10, -4.45621262e-10, -6.39133413e-10], [-3.58532593e-10, -4.43890119e-10, -6.39311493e-10], [-3.59802993e-10, -4.41411629e-10, -6.60119959e-10], [-3.58949426e-10, -4.43239168e-10, -6.37624564e-10], [-3.60242253e-10, -4.42435810e-10, -6.33906649e-10], [-3.61484953e-10, -4.32591463e-10, -6.34564401e-10], [-3.57910229e-10, -4.29199953e-10, -6.46178833e-10], [-3.57660401e-10, -4.23346747e-10, -6.13737727e-10], [-3.55238977e-10, -4.25607383e-10, -5.97280669e-10], [-3.52947865e-10, -4.24758007e-10, -5.87040860e-10], [-3.49784535e-10, -4.23031971e-10, -5.85980042e-10], [-3.53015478e-10, -4.23206359e-10, -6.03543548e-10], ... [-5.11393239e-09, -1.35741001e-08, -1.60814352e-07], [-5.44395062e-09, -2.43575133e-08, -1.45002247e-07], [-4.86894791e-09, -4.12963530e-08, -1.47366620e-07], [-5.39292611e-09, -5.72370809e-08, -1.25824272e-07], [-6.41620401e-09, -6.45678071e-08, -1.10190449e-07], [-6.78160905e-09, -6.84167958e-08, -9.38289020e-08], [-7.29576932e-09, -6.47739071e-08, -7.68193615e-08], [-8.18336510e-09, -5.81149209e-08, -7.04859318e-08], [-7.76166331e-09, -5.19686871e-08, -6.43213554e-08], [-7.04196568e-09, -4.70384727e-08, -6.16404350e-08], [-7.90036125e-09, -4.03301179e-08, -6.00140240e-08], [-4.88817342e-09, -1.86461691e-08, -3.97448545e-08], [-7.09699721e-09, -1.72511705e-09, -1.00553397e-08], [-4.06853129e-09, -1.28853817e-09, -2.58423283e-09], [-2.98437519e-09, -1.13034027e-09, -3.60601060e-09], [-1.09980309e-08, -2.05348050e-09, -5.57511981e-10], [-1.15927854e-08, -4.67873607e-09, -3.97649857e-10], [-1.14538530e-08, -5.87054716e-09, -3.38541611e-10], [-1.07085452e-08, -6.21498941e-09, -4.18006518e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.5587 -0.7393 ... -28.37 -5.81
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.55872717, -0.73933429, -1.01263507], [ -0.55909885, -0.74781458, -1.02032381], [ -0.56339972, -0.75235563, -1.04114909], [ -0.56574466, -0.75408049, -1.00271978], [ -0.56327974, -0.74221028, -0.9980942 ], [ -0.55951453, -0.73760955, -1.01441338], [ -0.55592129, -0.73105519, -0.98191639], [ -0.55362725, -0.72750384, -1.02130405], [ -0.56301865, -0.7252834 , -1.01789328], [ -0.55946494, -0.71693061, -1.03545581], [ -0.55886687, -0.71391623, -1.07142686], [ -0.55816062, -0.71663231, -1.04043361], [ -0.56094336, -0.71781622, -1.06883406], [ -0.55902317, -0.70272396, -1.05033251], [ -0.55030415, -0.69910558, -1.06303512], [ -0.55435328, -0.68816715, -0.99056348], [ -0.54662938, -0.68960124, -0.96040917], [ -0.5458248 , -0.68627143, -0.95080166], [ -0.54284984, -0.67667759, -0.94810626], [ -0.54519989, -0.67693639, -0.979253 ], ... [ -22.76061424, -33.70290482, -94.29752641], [ -20.68569739, -42.29942 , -335.72166172], [ -21.75314978, -72.79134959, -352.68323712], [ -20.33633891, -130.71658974, -353.91620765], [ -20.80126538, -172.30126404, -320.39020802], [ -22.98944112, -190.17481245, -293.028654 ], [ -25.53994451, -196.43233475, -267.77275736], [ -28.77743047, -187.48167067, -249.95806384], [ -32.94550126, -175.95360989, -226.59814176], [ -33.82984713, -164.83311868, -216.09387751], [ -33.77147363, -156.69712408, -208.27023105], [ -35.7552922 , -146.57650858, -204.76277986], [ -25.48603855, -75.301242 , -140.61098295], [ -33.06930083, -12.40822579, -42.20722391], [ -26.257377 , -10.45363752, -15.4468764 ], [ -24.46266471, -5.90238543, -13.88000668], [ -40.2315518 , -17.05878402, -4.50023447], [ -42.74567498, -27.01418861, -3.93155364], [ -40.26200269, -28.88526491, -3.4606702 ], [ -36.62022982, -28.36562722, -5.8097894 ]]]) - S2(depth, hour, tau_bins)float320.000118 0.0002866 ... 2.19e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.18035634e-04, 2.86617229e-04, 3.68310226e-04], [1.19055607e-04, 2.93288234e-04, 3.68738431e-04], [1.21223944e-04, 2.90943921e-04, 3.86649830e-04], [1.24606799e-04, 2.89873598e-04, 4.03484708e-04], [1.22047291e-04, 2.80318171e-04, 4.00653458e-04], [1.19750825e-04, 2.80132226e-04, 3.87836946e-04], [1.17275791e-04, 2.74971506e-04, 3.79573670e-04], [1.19338751e-04, 2.72942707e-04, 3.62854218e-04], [1.24937898e-04, 2.71977799e-04, 3.54683434e-04], [1.28315500e-04, 2.71855562e-04, 3.48416914e-04], [1.28547457e-04, 2.69131095e-04, 3.54306278e-04], [1.29285705e-04, 2.68015225e-04, 3.56286910e-04], [1.28866639e-04, 2.66737159e-04, 3.46874818e-04], [1.24962899e-04, 2.58250860e-04, 3.61833983e-04], [1.19088931e-04, 2.55677296e-04, 3.59144819e-04], [1.16096089e-04, 2.48583849e-04, 3.58901307e-04], [1.14474693e-04, 2.44785100e-04, 3.55322234e-04], [1.08996122e-04, 2.44438765e-04, 3.55026539e-04], [1.02533719e-04, 2.44985917e-04, 3.44370579e-04], [1.07392145e-04, 2.47505552e-04, 3.61505779e-04], ... [7.28670711e-05, 6.50857328e-05, 6.72916212e-05], [7.45321959e-05, 8.67422787e-05, 4.64802906e-05], [7.49734172e-05, 9.21883038e-05, 2.75155944e-05], [7.82151910e-05, 9.42244806e-05, 1.68252045e-05], [8.51124059e-05, 7.84320728e-05, 1.13622445e-05], [8.91593227e-05, 6.00015410e-05, 8.76838749e-06], [9.46019863e-05, 4.17266747e-05, 6.79051436e-06], [9.57649972e-05, 2.93372104e-05, 5.51613903e-06], [9.58165911e-05, 2.10807702e-05, 4.71648173e-06], [9.64381688e-05, 1.72020154e-05, 4.28609746e-06], [9.73792412e-05, 1.46298644e-05, 4.15710019e-06], [9.31051254e-05, 1.27367175e-05, 4.07165271e-06], [1.01596459e-04, 1.21337544e-05, 3.59626006e-06], [1.06722146e-04, 3.36617632e-05, 3.80803567e-06], [1.02554128e-04, 4.74796179e-05, 5.74421392e-06], [9.62065242e-05, 5.39669963e-05, 6.05823743e-06], [9.68023087e-05, 5.40903638e-05, 8.59525062e-06], [9.29079615e-05, 5.20505928e-05, 1.37420657e-05], [8.81354208e-05, 4.90190614e-05, 1.74025445e-05], [8.50617798e-05, 4.72261891e-05, 2.19031808e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002707 0.0002679 ... 1.066e-05
- long_name :
- $N^2$
- units :
- s$^{-2}$
array([[[2.70653982e-04, 2.67854164e-04, 2.60556408e-04], [2.70489603e-04, 2.67931056e-04, 2.61637673e-04], [2.68508651e-04, 2.68075935e-04, 2.64729460e-04], [2.66491756e-04, 2.67974130e-04, 2.71308294e-04], [2.67325930e-04, 2.67938973e-04, 2.70344783e-04], [2.69618089e-04, 2.67519441e-04, 2.64699716e-04], [2.69445707e-04, 2.67475261e-04, 2.66230258e-04], [2.67186144e-04, 2.68850679e-04, 2.58856395e-04], [2.71684374e-04, 2.66605173e-04, 2.58436747e-04], [2.69634824e-04, 2.68864445e-04, 2.49675912e-04], [2.70803575e-04, 2.66544346e-04, 2.50487588e-04], [2.71231111e-04, 2.66110990e-04, 2.51161167e-04], [2.71857542e-04, 2.66223098e-04, 2.52874015e-04], [2.72617326e-04, 2.63849157e-04, 2.66338582e-04], [2.71545228e-04, 2.64220784e-04, 2.70796998e-04], [2.70907389e-04, 2.63898313e-04, 2.71208119e-04], [2.73490848e-04, 2.62059155e-04, 2.72291130e-04], [2.73298821e-04, 2.62513553e-04, 2.72275705e-04], [2.71756551e-04, 2.64893140e-04, 2.73101410e-04], [2.73922691e-04, 2.64201226e-04, 2.70985212e-04], ... [2.66783991e-05, 2.24435680e-05, 2.01482108e-05], [2.76282153e-05, 2.52834870e-05, 1.53130677e-05], [2.78609550e-05, 2.53739199e-05, 1.07697870e-05], [2.88161682e-05, 2.38241410e-05, 7.61099909e-06], [3.00592146e-05, 1.97585177e-05, 5.86610713e-06], [3.10059622e-05, 1.59887131e-05, 5.00141596e-06], [3.13461569e-05, 1.26825598e-05, 4.25584130e-06], [3.19165847e-05, 9.92587957e-06, 3.65537994e-06], [3.14851823e-05, 7.92554147e-06, 3.33652520e-06], [3.05563590e-05, 6.80374478e-06, 3.12953216e-06], [3.05634167e-05, 6.46581339e-06, 3.12648399e-06], [3.00894790e-05, 5.96192513e-06, 3.03067145e-06], [3.18050443e-05, 5.42599446e-06, 3.01429736e-06], [3.27169000e-05, 9.65514482e-06, 2.92601317e-06], [3.30679140e-05, 1.31469769e-05, 3.74685192e-06], [3.10162686e-05, 1.42103781e-05, 4.23103938e-06], [3.14569043e-05, 1.57434079e-05, 5.41504414e-06], [3.13854980e-05, 1.62794604e-05, 7.40211590e-06], [3.05814792e-05, 1.66734153e-05, 9.08484435e-06], [2.97653733e-05, 1.71468819e-05, 1.06635689e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float321.866 0.6854 ... 0.4224 0.5125
- long_name :
- $Ri^g$
array([[[1.8662848 , 0.68544173, 0.5350752 ], [1.8600645 , 0.6764416 , 0.52997637], [1.8082513 , 0.6684065 , 0.51994437], [1.7514868 , 0.66514564, 0.53821963], [1.7742116 , 0.6801465 , 0.5362674 ], [1.8050451 , 0.6883631 , 0.5284693 ], [1.826543 , 0.6967864 , 0.53718793], [1.839041 , 0.69765747, 0.538539 ], [1.8039743 , 0.69942325, 0.54343367], [1.762919 , 0.7034895 , 0.5462879 ], [1.7395374 , 0.71042335, 0.5473652 ], [1.743981 , 0.7068553 , 0.5498181 ], [1.7485907 , 0.7094732 , 0.5552983 ], [1.800962 , 0.7201221 , 0.56303406], [1.8770511 , 0.7300272 , 0.5768663 ], [1.8958896 , 0.7441837 , 0.59068596], [1.9319459 , 0.7465268 , 0.6032988 ], [1.9969598 , 0.76601106, 0.59619606], [2.1091611 , 0.77056324, 0.59924066], [2.072525 , 0.76344836, 0.5768527 ], ... [0.417879 , 0.3804391 , 0.3238179 ], [0.41090554, 0.30941188, 0.3488606 ], [0.403327 , 0.30385834, 0.392285 ], [0.39265534, 0.28617096, 0.46646762], [0.36620998, 0.2877478 , 0.5247332 ], [0.34303582, 0.31285378, 0.5649361 ], [0.3227075 , 0.34359443, 0.61189747], [0.3142567 , 0.38431424, 0.65183944], [0.30991292, 0.41828164, 0.6832897 ], [0.31734043, 0.44741976, 0.71992517], [0.32099736, 0.47542647, 0.747839 ], [0.33062443, 0.4994597 , 0.74357045], [0.32144618, 0.5118849 , 0.80607224], [0.31450492, 0.352032 , 0.76655614], [0.33332175, 0.34234598, 0.6389044 ], [0.35182685, 0.34710622, 0.6787852 ], [0.3533942 , 0.3665613 , 0.63400173], [0.36926734, 0.38762894, 0.5510011 ], [0.38271207, 0.4089583 , 0.52773184], [0.39180535, 0.42236108, 0.5125109 ]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float321.75 0.6778 0.516 ... 0.2708 0.353
- long_name :
- $Ri^g_T$
array([[[1.7498139 , 0.6778394 , 0.5160031 ], [1.7240694 , 0.6695879 , 0.49940598], [1.6583761 , 0.6628067 , 0.498627 ], [1.6018262 , 0.658349 , 0.51457024], [1.6139219 , 0.66992456, 0.52054894], [1.6932245 , 0.6747377 , 0.5110692 ], [1.7320414 , 0.68135345, 0.5248196 ], [1.7112534 , 0.6885524 , 0.52049387], [1.6603553 , 0.69147515, 0.5276542 ], [1.6072863 , 0.6940439 , 0.542287 ], [1.5864432 , 0.70075834, 0.5364443 ], [1.586266 , 0.70577735, 0.54150844], [1.5927796 , 0.70689046, 0.5504924 ], [1.6602877 , 0.7092931 , 0.55996585], [1.7805405 , 0.7184469 , 0.5745784 ], [1.8147937 , 0.7288473 , 0.58771074], [1.8759367 , 0.7344651 , 0.59955335], [1.9077746 , 0.7484478 , 0.59346616], [1.9881756 , 0.7515857 , 0.5964693 ], [1.9673991 , 0.7486092 , 0.56754345], ... [0.2634116 , 0.2507721 , 0.24641225], [0.2607986 , 0.21899809, 0.26634485], [0.25649 , 0.2143133 , 0.2940078 ], [0.25049907, 0.20534892, 0.32926372], [0.23761362, 0.20744732, 0.35544658], [0.22342424, 0.21846932, 0.37724414], [0.21559256, 0.2321828 , 0.39727533], [0.21003583, 0.2461529 , 0.41424218], [0.21187592, 0.26617616, 0.4138385 ], [0.21543066, 0.27246305, 0.436479 ], [0.2142856 , 0.27827302, 0.4584359 ], [0.2159077 , 0.29173055, 0.43860143], [0.20547742, 0.29568854, 0.4857257 ], [0.21092497, 0.21182545, 0.4756171 ], [0.22476283, 0.2046566 , 0.37278977], [0.23309311, 0.20733707, 0.4022013 ], [0.23358554, 0.22945035, 0.39872727], [0.24027762, 0.24394931, 0.36718082], [0.24860656, 0.26064178, 0.36847693], [0.25379637, 0.27075374, 0.35295993]]], dtype=float32) - tau(hour, tau_bins)float320.0295 0.054 ... 0.05439 0.08311
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.0295027 , 0.05400424, 0.08233272], [0.029748 , 0.0539082 , 0.08225691], [0.02973902, 0.05381709, 0.08261511], [0.02958953, 0.05378674, 0.08290136], [0.02952781, 0.0541986 , 0.08320552], [0.02957567, 0.05422445, 0.08379569], [0.02954055, 0.05467816, 0.08413152], [0.02949499, 0.05448272, 0.08375749], [0.02946378, 0.05434905, 0.08394575], [0.02955062, 0.05434605, 0.08510967], [0.02943835, 0.05417863, 0.08425807], [0.02927817, 0.05410217, 0.08441167], [0.02894278, 0.05420254, 0.08438533], [0.02929234, 0.05503666, 0.08478092], [0.0293923 , 0.05574505, 0.08499894], [0.0296241 , 0.05632416, 0.08501871], [0.03005047, 0.05656263, 0.08522117], [0.03020069, 0.05679295, 0.08566844], [0.03010707, 0.05716396, 0.08595885], [0.02992886, 0.05671743, 0.08496676], [0.03006576, 0.05620772, 0.08470601], [0.02999885, 0.05570246, 0.08516616], [0.02975408, 0.05507209, 0.08390015], [0.02965912, 0.05439087, 0.0831105 ]], dtype=float32)
- title :
- baseline
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006551 eps (depth, hour, tau_bins) float32 2.922e-08 1.368e-07 ... 2.348e-08 chi (depth, hour, tau_bins) float32 3.153e-08 5.147e-08 ... 5.686e-09 Jb (depth, hour, tau_bins) float32 -3.583e-10 ... -4.18e-10 Jq (depth, hour, tau_bins) float64 -0.5587 -0.7393 ... -28.37 -5.81 S2 (depth, hour, tau_bins) float32 0.000118 0.0002866 ... 2.19e-05 N2 (depth, hour, tau_bins) float32 0.0002707 0.0002679 ... 1.066e-05 Rig (depth, hour, tau_bins) float32 1.866 0.6854 ... 0.4224 0.5125 Rig_T (depth, hour, tau_bins) float32 1.75 0.6778 0.516 ... 0.2708 0.353 tau (hour, tau_bins) float32 0.0295 0.054 0.08233 ... 0.05439 0.08311 Attributes: title: baselinebaseline- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006196
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062664e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], ... [8.69949930e-04, 9.59244964e-04, 1.11343141e-03], [8.50179931e-04, 1.02828699e-03, 1.24498047e-02], [8.22628965e-04, 1.05729094e-03, 3.77871096e-03], [8.18862813e-04, 1.26605667e-03, 2.33795643e-02], [8.30616336e-04, 1.36252248e-03, 3.00153233e-02], [8.26583593e-04, 1.66581431e-03, 3.50915715e-02], [8.35646526e-04, 2.89651891e-03, 4.03600074e-02], [8.40844936e-04, 5.73793054e-03, 4.29606661e-02], [8.83748115e-04, 8.86736996e-03, 4.64188121e-02], [8.89282324e-04, 1.00382138e-02, 4.53674309e-02], [9.06878675e-04, 1.16374400e-02, 4.41440344e-02], [9.52538336e-04, 1.18752746e-02, 4.22920287e-02], [9.77167627e-04, 3.11270822e-03, 2.84510609e-02], [1.07384217e-03, 1.43216818e-03, 3.58461030e-03], [9.83901555e-04, 1.30531006e-03, 1.26952492e-03], [9.94352158e-04, 8.17729277e-04, 1.27175078e-03], [1.12384127e-03, 1.34078134e-03, 5.45061426e-04], [1.12999673e-03, 1.29678287e-03, 6.57544471e-04], [1.10153994e-03, 1.23138377e-03, 6.13115670e-04], [1.06722605e-03, 1.15680229e-03, 6.19593775e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float323.067e-09 6.996e-09 ... 3.577e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[3.06701176e-09, 6.99570357e-09, 1.12792096e-08], [2.95429015e-09, 7.27236227e-09, 1.16628520e-08], [3.03435321e-09, 7.29928473e-09, 1.15865966e-08], [3.09861203e-09, 7.32230099e-09, 1.05787965e-08], [3.11136494e-09, 7.09674719e-09, 1.22516060e-08], [3.04559178e-09, 6.99951208e-09, 1.30705642e-08], [2.99432013e-09, 6.73037848e-09, 1.37911904e-08], [3.08337444e-09, 6.66707800e-09, 1.50147272e-08], [3.12555049e-09, 6.68693634e-09, 1.58194240e-08], [3.12062776e-09, 6.52366206e-09, 1.66755285e-08], [3.05749825e-09, 6.75726852e-09, 1.61235363e-08], [3.13664117e-09, 6.70460842e-09, 1.47471004e-08], [3.15217874e-09, 6.90299906e-09, 1.50834563e-08], [3.10377768e-09, 6.81277612e-09, 1.38752387e-08], [2.94250735e-09, 6.56541577e-09, 1.35846872e-08], [2.94795166e-09, 6.39441211e-09, 1.15830145e-08], [2.81318369e-09, 6.39819486e-09, 1.10841469e-08], [2.77422463e-09, 6.17787199e-09, 1.08094866e-08], [2.80508683e-09, 5.86984106e-09, 1.12552065e-08], [2.77357826e-09, 6.00636252e-09, 1.14595746e-08], ... [1.57990542e-07, 1.34023310e-07, 2.11007034e-07], [1.52075884e-07, 2.07358511e-07, 8.69410428e-07], [1.49262263e-07, 2.32231471e-07, 5.85107387e-07], [1.47833191e-07, 3.86520156e-07, 7.40398832e-07], [1.56316773e-07, 5.23080075e-07, 5.81518179e-07], [1.63320493e-07, 5.83236556e-07, 4.97904125e-07], [1.74322750e-07, 6.14305293e-07, 4.01600914e-07], [1.87730222e-07, 5.85696284e-07, 3.23046464e-07], [2.10503316e-07, 5.40947042e-07, 2.78580956e-07], [2.11623188e-07, 4.88402975e-07, 2.46581408e-07], [2.17197865e-07, 4.53600649e-07, 2.35122315e-07], [2.25392967e-07, 4.29075641e-07, 2.21211138e-07], [2.16730115e-07, 3.21438961e-07, 1.52973996e-07], [2.28090641e-07, 2.25466991e-07, 6.30578967e-08], [1.96342341e-07, 2.48357935e-07, 4.92314278e-08], [1.94988189e-07, 1.77086974e-07, 4.40254162e-08], [2.22069204e-07, 2.16667615e-07, 2.62699125e-08], [2.26215022e-07, 1.92706182e-07, 3.23837241e-08], [2.20546781e-07, 1.71657675e-07, 3.60358072e-08], [2.06545138e-07, 1.56897627e-07, 3.57718406e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float321.417e-08 1.12e-08 ... 1.021e-08
- long_name :
- $χ$
- units :
- C^2/s
array([[[1.41654599e-08, 1.12043317e-08, 1.14997345e-08], [1.43564414e-08, 1.10966809e-08, 1.06867253e-08], [1.40540273e-08, 1.12314247e-08, 1.03123279e-08], [1.41355176e-08, 1.13349161e-08, 1.00236583e-08], [1.42605980e-08, 1.10975300e-08, 1.07465459e-08], [1.42887382e-08, 1.11918066e-08, 1.07850138e-08], [1.43708263e-08, 1.12686127e-08, 1.08477582e-08], [1.42204337e-08, 1.12907834e-08, 1.09332676e-08], [1.43949102e-08, 1.13406422e-08, 1.08743325e-08], [1.42460177e-08, 1.14293757e-08, 1.04407292e-08], [1.40131853e-08, 1.15438645e-08, 1.04089919e-08], [1.38146916e-08, 1.15858061e-08, 1.04847828e-08], [1.37048985e-08, 1.15371819e-08, 1.06222746e-08], [1.38580338e-08, 1.15255503e-08, 1.07217994e-08], [1.39460585e-08, 1.15467724e-08, 1.07971161e-08], [1.40707135e-08, 1.14792176e-08, 1.11246070e-08], [1.44085774e-08, 1.13738761e-08, 1.11539817e-08], [1.43768464e-08, 1.14945884e-08, 1.12381482e-08], [1.44178420e-08, 1.15233316e-08, 1.11600116e-08], [1.44640957e-08, 1.12931424e-08, 1.13877565e-08], ... [1.43766911e-07, 1.03006442e-07, 8.78815598e-08], [1.37191023e-07, 1.83328794e-07, 7.09568042e-07], [1.25446263e-07, 1.67309494e-07, 3.59490400e-07], [1.29034063e-07, 2.71363888e-07, 5.55303927e-07], [1.28467946e-07, 3.13767430e-07, 4.10859371e-07], [1.31779615e-07, 3.50028699e-07, 3.47528783e-07], [1.37602086e-07, 3.64599771e-07, 2.63568069e-07], [1.47534877e-07, 3.30650295e-07, 2.13382762e-07], [1.61122401e-07, 2.85556155e-07, 1.73867647e-07], [1.52407182e-07, 2.44686731e-07, 1.56882791e-07], [1.51709003e-07, 2.20665001e-07, 1.45941712e-07], [1.50763512e-07, 1.98459645e-07, 1.46552651e-07], [1.29097174e-07, 8.24987865e-08, 7.70501956e-08], [1.57343763e-07, 4.65327865e-08, 1.47697543e-08], [1.21017209e-07, 3.88323684e-08, 6.95555213e-09], [1.44461794e-07, 2.23305978e-08, 6.47459286e-09], [1.90294784e-07, 1.18683403e-07, 3.80536269e-09], [2.08375866e-07, 1.24235868e-07, 7.61631114e-09], [2.02433256e-07, 1.21694356e-07, 1.00547233e-08], [1.91222796e-07, 1.17619521e-07, 1.02139150e-08]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-2.363e-10 ... -8.176e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[-2.36305003e-10, -2.03665917e-10, -2.08056850e-10], [-2.36786424e-10, -2.02221850e-10, -2.09279122e-10], [-2.33130792e-10, -2.03391748e-10, -2.06526990e-10], [-2.32916963e-10, -2.05656311e-10, -2.00404207e-10], [-2.33894487e-10, -2.05576237e-10, -2.02458952e-10], [-2.35067965e-10, -2.06578477e-10, -2.01200598e-10], [-2.36723557e-10, -2.07841605e-10, -1.97894201e-10], [-2.34675418e-10, -2.07456149e-10, -2.04126174e-10], [-2.35204634e-10, -2.06371822e-10, -2.01659162e-10], [-2.33386255e-10, -2.09510145e-10, -1.95423525e-10], [-2.32901309e-10, -2.10196471e-10, -1.95556377e-10], [-2.29793545e-10, -2.10664777e-10, -1.96119843e-10], [-2.28470382e-10, -2.09507939e-10, -1.99778888e-10], [-2.31024630e-10, -2.09797971e-10, -1.99606068e-10], [-2.35091085e-10, -2.09259596e-10, -1.99032207e-10], [-2.39884834e-10, -2.09699591e-10, -2.02228262e-10], [-2.45257092e-10, -2.07174472e-10, -2.03287082e-10], [-2.45073906e-10, -2.07818360e-10, -2.04217240e-10], [-2.46258847e-10, -2.07750095e-10, -2.05482159e-10], [-2.46289100e-10, -2.06136733e-10, -2.08115400e-10], ... [-1.06592415e-08, -1.59663145e-08, -1.32835780e-07], [-1.03445750e-08, -1.54878776e-08, -5.95220513e-08], [-1.09041141e-08, -2.38682460e-08, -1.59961715e-07], [-1.09902807e-08, -3.03844914e-08, -1.49897033e-07], [-1.09048992e-08, -3.81727965e-08, -1.61086376e-07], [-1.12891199e-08, -5.17055732e-08, -1.47693413e-07], [-1.13750245e-08, -5.63804861e-08, -1.32643848e-07], [-1.32148603e-08, -6.07146688e-08, -1.20656594e-07], [-1.23768604e-08, -5.72766012e-08, -1.13463010e-07], [-1.22800037e-08, -5.40147020e-08, -1.14951519e-07], [-1.20845041e-08, -4.98633597e-08, -1.05718762e-07], [-9.85280746e-09, -2.33101858e-08, -5.91517733e-08], [-1.16605072e-08, -4.99037567e-09, -8.04534306e-09], [-7.44766782e-09, -3.38226780e-09, -2.08077888e-09], [-8.68693206e-09, -1.48512147e-09, -3.18790239e-09], [-1.40054999e-08, -9.78285009e-09, -3.67206598e-10], [-1.44412642e-08, -1.08139648e-08, -5.36393763e-10], [-1.42424401e-08, -1.11552581e-08, -6.93334057e-10], [-1.40376359e-08, -1.11661604e-08, -8.17562462e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.3639 -0.3232 ... -34.57 -7.167
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[-3.63856336e-01, -3.23210307e-01, -3.25944868e-01], [-3.64410742e-01, -3.22438626e-01, -3.14459372e-01], [-3.60407654e-01, -3.23780796e-01, -3.09333925e-01], [-3.60840765e-01, -3.25704081e-01, -3.03560250e-01], [-3.63860861e-01, -3.23031085e-01, -3.12243092e-01], [-3.65510015e-01, -3.23045562e-01, -3.13675629e-01], [-3.65869896e-01, -3.24214106e-01, -3.16109657e-01], [-3.64828718e-01, -3.25053940e-01, -3.17998895e-01], [-3.66923519e-01, -3.24808720e-01, -3.16305131e-01], [-3.64535768e-01, -3.27514952e-01, -3.11441584e-01], [-3.62548931e-01, -3.28812389e-01, -3.11511710e-01], [-3.59663507e-01, -3.29657957e-01, -3.14064940e-01], [-3.57138041e-01, -3.28465274e-01, -3.13897933e-01], [-3.58997834e-01, -3.27921523e-01, -3.16069328e-01], [-3.61749182e-01, -3.27854408e-01, -3.15479682e-01], [-3.61282345e-01, -3.26603508e-01, -3.20980527e-01], [-3.66450614e-01, -3.26124243e-01, -3.21068923e-01], [-3.64773864e-01, -3.27023533e-01, -3.22861464e-01], [-3.65693197e-01, -3.27779466e-01, -3.23091044e-01], [-3.66243552e-01, -3.25203886e-01, -3.25132215e-01], ... [-3.11515463e+01, -2.95920814e+01, -3.13240759e+01], [-2.97588069e+01, -4.01929936e+01, -3.26898750e+02], [-2.80524053e+01, -3.79606483e+01, -1.52565274e+02], [-2.87241693e+01, -5.96539726e+01, -3.31737156e+02], [-2.92523879e+01, -7.74188050e+01, -3.17598502e+02], [-2.97933349e+01, -1.03626681e+02, -3.14021997e+02], [-3.06915511e+01, -1.33709094e+02, -2.94227293e+02], [-3.16197143e+01, -1.50164779e+02, -2.75888518e+02], [-3.48487573e+01, -1.54185040e+02, -2.59916040e+02], [-3.53652229e+01, -1.47535313e+02, -2.41424957e+02], [-3.63852525e+01, -1.43487593e+02, -2.32847570e+02], [-3.87944462e+01, -1.38562537e+02, -2.20087752e+02], [-3.47095415e+01, -6.84364904e+01, -1.35048197e+02], [-3.79342051e+01, -2.57598461e+01, -2.68607238e+01], [-3.00724935e+01, -1.82040015e+01, -1.15180749e+01], [-3.35407877e+01, -9.14485579e+00, -9.79402621e+00], [-4.07352508e+01, -3.76753676e+01, -3.51846569e+00], [-4.24517449e+01, -3.83129382e+01, -5.99288931e+00], [-4.14190661e+01, -3.68610034e+01, -7.49174915e+00], [-3.99809860e+01, -3.45665587e+01, -7.16703748e+00]]]) - S2(depth, hour, tau_bins)float321.608e-05 3.475e-05 ... 4.999e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.60773343e-05, 3.47473433e-05, 5.45502517e-05], [1.55510897e-05, 3.62657956e-05, 5.80403103e-05], [1.57532741e-05, 3.61047132e-05, 5.88809962e-05], [1.61362404e-05, 3.65621709e-05, 5.16177024e-05], [1.63347595e-05, 3.55188567e-05, 6.21003710e-05], [1.59758565e-05, 3.49079783e-05, 6.53139577e-05], [1.57272680e-05, 3.38322498e-05, 6.75400515e-05], [1.60485488e-05, 3.36952726e-05, 7.21537217e-05], [1.64259400e-05, 3.36227749e-05, 6.97661089e-05], [1.64107878e-05, 3.27744456e-05, 8.03719886e-05], [1.60798663e-05, 3.40344159e-05, 7.83935102e-05], [1.63162404e-05, 3.36289268e-05, 7.09879023e-05], [1.63955192e-05, 3.41706000e-05, 7.23957492e-05], [1.62350207e-05, 3.38442551e-05, 6.78550277e-05], [1.52499942e-05, 3.28904680e-05, 6.58997596e-05], [1.51459171e-05, 3.21342086e-05, 5.73855250e-05], [1.46635284e-05, 3.17903687e-05, 5.46010815e-05], [1.46638777e-05, 3.11573203e-05, 5.30258621e-05], [1.46625471e-05, 2.97258357e-05, 5.51550838e-05], [1.46957009e-05, 3.03876823e-05, 5.71436394e-05], ... [1.76954345e-04, 1.31229055e-04, 1.19671509e-04], [1.75386085e-04, 1.44285470e-04, 9.24286651e-05], [1.76493719e-04, 1.65989506e-04, 9.93364883e-05], [1.77259906e-04, 1.83417767e-04, 6.67022250e-05], [1.80237097e-04, 2.02479947e-04, 4.05998107e-05], [1.86967096e-04, 2.01381699e-04, 2.87006660e-05], [1.91430430e-04, 1.85397657e-04, 2.00318373e-05], [1.97789792e-04, 1.57587536e-04, 1.56843635e-05], [2.03315052e-04, 1.19843135e-04, 1.30644439e-05], [2.03890755e-04, 9.93930225e-05, 1.25701235e-05], [2.09443897e-04, 8.31915386e-05, 1.15447201e-05], [2.06869125e-04, 7.98302353e-05, 1.13064698e-05], [2.16365268e-04, 1.05084415e-04, 9.60457783e-06], [2.22570961e-04, 1.54111607e-04, 1.23858135e-05], [2.14604661e-04, 1.80870440e-04, 1.95051653e-05], [2.11807986e-04, 1.81155774e-04, 2.01697949e-05], [2.13664738e-04, 1.67245424e-04, 2.97912757e-05], [2.12474784e-04, 1.55046437e-04, 3.86240499e-05], [2.08408193e-04, 1.45126833e-04, 4.46427548e-05], [2.01206974e-04, 1.38201241e-04, 4.99901362e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002237 0.0002014 ... 1.187e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.23716459e-04, 2.01369432e-04, 2.07364807e-04], [2.24670584e-04, 2.00559298e-04, 2.04587486e-04], [2.22044764e-04, 2.02383279e-04, 2.01458635e-04], [2.21417606e-04, 2.03395641e-04, 1.95091299e-04], [2.21755530e-04, 2.02223877e-04, 2.01670875e-04], [2.21897033e-04, 2.02601805e-04, 2.01380739e-04], [2.21933631e-04, 2.02978175e-04, 2.01497838e-04], [2.20619098e-04, 2.03436794e-04, 2.02596842e-04], [2.21749637e-04, 2.02988711e-04, 2.01417221e-04], [2.19857873e-04, 2.04759723e-04, 1.99296846e-04], [2.19346490e-04, 2.05749850e-04, 1.98707756e-04], [2.18359230e-04, 2.06736469e-04, 2.01719973e-04], [2.18257366e-04, 2.06634082e-04, 1.97994610e-04], [2.19672744e-04, 2.05267948e-04, 2.00647381e-04], [2.20484260e-04, 2.05869466e-04, 1.96984562e-04], [2.22277275e-04, 2.05256205e-04, 2.00871931e-04], [2.22674425e-04, 2.04421536e-04, 2.00914845e-04], [2.22843708e-04, 2.05170858e-04, 2.03752279e-04], [2.24134274e-04, 2.05697404e-04, 2.03576696e-04], [2.23726267e-04, 2.04792828e-04, 2.06141849e-04], ... [4.44578545e-05, 3.09555980e-05, 2.18656496e-05], [4.37544404e-05, 3.26075969e-05, 2.38331431e-05], [4.39932483e-05, 3.30363655e-05, 2.08300789e-05], [4.41018747e-05, 3.41599880e-05, 1.92035659e-05], [4.40224831e-05, 3.48551039e-05, 1.46976718e-05], [4.41208649e-05, 3.49741385e-05, 1.14134200e-05], [4.50448970e-05, 3.32406053e-05, 8.23586197e-06], [4.56341149e-05, 2.86893446e-05, 7.04849163e-06], [4.63023462e-05, 2.27672244e-05, 6.17078695e-06], [4.63085671e-05, 2.06949808e-05, 5.77945048e-06], [4.70361374e-05, 1.78653881e-05, 5.42836233e-06], [4.65924677e-05, 1.72670952e-05, 5.22179744e-06], [4.66712881e-05, 1.52227567e-05, 4.89958938e-06], [4.81022325e-05, 1.52632801e-05, 4.34061121e-06], [4.82442083e-05, 2.59471744e-05, 4.41810153e-06], [4.77407229e-05, 2.84563885e-05, 5.11112830e-06], [4.76316200e-05, 2.91304204e-05, 5.96908694e-06], [4.79773953e-05, 2.88553783e-05, 8.46229523e-06], [4.80109811e-05, 2.87334278e-05, 1.05174622e-05], [4.71629573e-05, 2.89586733e-05, 1.18731123e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float3213.2 4.297 3.056 ... 0.2232 0.2512
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[13.197857 , 4.2973194 , 3.0555031 ], [13.821407 , 4.232544 , 2.5504222 ], [13.510264 , 4.2386236 , 2.4945803 ], [13.20141 , 4.2471313 , 2.4653025 ], [13.149447 , 4.319394 , 2.4220955 ], [13.85933 , 4.4229817 , 2.3923457 ], [14.107593 , 4.5487447 , 2.3519185 ], [13.647481 , 4.598955 , 2.3381877 ], [13.462015 , 4.5880175 , 2.3576286 ], [13.113719 , 4.6214905 , 2.182707 ], [13.353025 , 4.4489136 , 2.2182493 ], [13.087497 , 4.5611644 , 2.2721744 ], [12.944699 , 4.5314035 , 2.3206887 ], [12.845064 , 4.613967 , 2.453578 ], [13.476563 , 4.813492 , 2.4708343 ], [13.658272 , 5.07368 , 2.7000542 ], [14.541157 , 5.212734 , 2.7889817 ], [14.787399 , 5.2871675 , 2.8942657 ], [15.181756 , 5.5781765 , 2.8768818 ], [15.022156 , 5.3412776 , 2.904497 ], ... [ 0.2605168 , 0.24626791, 0.2186516 ], [ 0.26234898, 0.24100047, 0.27151155], [ 0.26460433, 0.22231369, 0.22628295], [ 0.26444855, 0.21137132, 0.32527298], [ 0.26379338, 0.2065137 , 0.37389553], [ 0.26258442, 0.20844041, 0.3997121 ], [ 0.25847906, 0.22196066, 0.4238111 ], [ 0.25546002, 0.2310276 , 0.44557902], [ 0.24929616, 0.24503568, 0.47095898], [ 0.25088966, 0.25116956, 0.47218096], [ 0.25009263, 0.25732547, 0.46698648], [ 0.24756661, 0.26599523, 0.46304277], [ 0.22898337, 0.22704817, 0.5012692 ], [ 0.21460304, 0.16282685, 0.3467214 ], [ 0.22194551, 0.16865185, 0.23832142], [ 0.223914 , 0.17884398, 0.23131603], [ 0.22466302, 0.19137523, 0.21537249], [ 0.22704174, 0.20382346, 0.23264292], [ 0.23163387, 0.21370158, 0.24267618], [ 0.2359288 , 0.22320737, 0.25116214]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float3213.39 4.173 3.016 ... 0.1836 0.2279
- long_name :
- $Ri^g_T$
array([[[13.39003 , 4.1733675 , 3.0161352 ], [14.119032 , 4.050316 , 2.7403107 ], [13.708002 , 4.044218 , 2.772739 ], [13.35049 , 4.09664 , 2.7337017 ], [13.440435 , 4.2385445 , 2.5692105 ], [13.888355 , 4.367624 , 2.4463243 ], [14.08926 , 4.548192 , 2.2969518 ], [13.814713 , 4.5322876 , 2.2213016 ], [13.61069 , 4.5070376 , 2.2090302 ], [13.551187 , 4.5452027 , 2.0633187 ], [13.787437 , 4.4257965 , 2.0763931 ], [13.39185 , 4.480337 , 2.1504233 ], [13.147581 , 4.426073 , 2.1903646 ], [13.178958 , 4.545905 , 2.2907367 ], [13.68602 , 4.709573 , 2.301371 ], [13.814043 , 4.880429 , 2.4320097 ], [14.562265 , 5.046787 , 2.570214 ], [14.989765 , 5.1713166 , 2.7265682 ], [15.491265 , 5.3896675 , 2.7282755 ], [15.379088 , 5.16234 , 2.7647042 ], ... [ 0.19949941, 0.19074827, 0.17702514], [ 0.20222469, 0.18197736, 0.18649122], [ 0.20144732, 0.16863236, 0.17766082], [ 0.20056407, 0.15703726, 0.18814558], [ 0.20053883, 0.14603414, 0.20615524], [ 0.1938774 , 0.14094096, 0.23102301], [ 0.18646285, 0.14410391, 0.25327593], [ 0.1793328 , 0.14658612, 0.28199226], [ 0.16956541, 0.1529384 , 0.29382858], [ 0.16857266, 0.15509175, 0.3094122 ], [ 0.16314791, 0.15785547, 0.30625468], [ 0.16122043, 0.16049628, 0.3079362 ], [ 0.15586448, 0.14820942, 0.3269163 ], [ 0.15939882, 0.13319623, 0.2494062 ], [ 0.16583118, 0.13827197, 0.19804096], [ 0.1705277 , 0.1412782 , 0.19478822], [ 0.17406169, 0.15591256, 0.21430875], [ 0.17862558, 0.16688593, 0.21968636], [ 0.1818856 , 0.17532237, 0.22169115], [ 0.18481219, 0.18359019, 0.22792467]]], dtype=float32) - tau(hour, tau_bins)float320.02931 0.05352 ... 0.0542 0.08266
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02931377, 0.05352184, 0.08240295], [0.02966809, 0.05379798, 0.08218867], [0.02963378, 0.0531844 , 0.08235388], [0.02950549, 0.05342022, 0.08332323], [0.02959662, 0.05367155, 0.08316955], [0.0294683 , 0.05404468, 0.08309559], [0.02944375, 0.05421706, 0.08337168], [0.02952339, 0.05432522, 0.08384206], [0.02939557, 0.05427076, 0.08357246], [0.02919702, 0.05400485, 0.08424108], [0.02917114, 0.05389731, 0.08418184], [0.02895542, 0.05386308, 0.08414546], [0.02882244, 0.05380915, 0.0840569 ], [0.02902017, 0.05480089, 0.08480587], [0.02929266, 0.05536883, 0.08480069], [0.02949213, 0.05573643, 0.08509558], [0.02968458, 0.05615301, 0.08533888], [0.02993085, 0.05675283, 0.08584618], [0.02995646, 0.05718726, 0.08638418], [0.02968866, 0.05656635, 0.08475825], [0.0296479 , 0.05585978, 0.08439424], [0.02981201, 0.05550362, 0.08500274], [0.02963592, 0.0546338 , 0.08361351], [0.02963188, 0.05420088, 0.08266187]], dtype=float32)
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006196 eps (depth, hour, tau_bins) float32 3.067e-09 6.996e-09 ... 3.577e-08 chi (depth, hour, tau_bins) float32 1.417e-08 1.12e-08 ... 1.021e-08 Jb (depth, hour, tau_bins) float32 -2.363e-10 ... -8.176e-10 Jq (depth, hour, tau_bins) float64 -0.3639 -0.3232 ... -34.57 -7.167 S2 (depth, hour, tau_bins) float32 1.608e-05 3.475e-05 ... 4.999e-05 N2 (depth, hour, tau_bins) float32 0.0002237 0.0002014 ... 1.187e-05 Rig (depth, hour, tau_bins) float32 13.2 4.297 3.056 ... 0.2232 0.2512 Rig_T (depth, hour, tau_bins) float32 13.39 4.173 ... 0.1836 0.2279 tau (hour, tau_bins) float32 0.02931 0.05352 0.0824 ... 0.0542 0.08266 Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5kpp.lmd.004- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 0.000262 ... 0.0003525
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062653e-06, 2.62037240e-04, 1.05890748e-03], [1.00062664e-06, 2.72701523e-04, 1.09908008e-03], [1.00062664e-06, 2.90863565e-04, 1.02824823e-03], [1.00062664e-06, 2.96896702e-04, 1.00071402e-03], [1.00062664e-06, 2.66355200e-04, 1.02508185e-03], [1.00062653e-06, 2.53402599e-04, 9.46604297e-04], [1.00062653e-06, 2.26842472e-04, 8.61362787e-04], [1.00062653e-06, 2.20032278e-04, 8.83506727e-04], [1.00062664e-06, 2.11917271e-04, 8.30798468e-04], [1.00062664e-06, 2.03725998e-04, 8.30619712e-04], [1.00062675e-06, 1.92588341e-04, 8.49988079e-04], [1.00062675e-06, 1.90688588e-04, 8.54582933e-04], [1.00062675e-06, 1.91088926e-04, 7.87007099e-04], [1.00062664e-06, 1.57940449e-04, 7.20337499e-04], [1.00062653e-06, 1.38884861e-04, 6.53320050e-04], [1.00062653e-06, 1.24576676e-04, 6.12310017e-04], [1.00062653e-06, 1.00052304e-04, 7.07846077e-04], [1.00062641e-06, 9.21100873e-05, 7.37336930e-04], [1.00062641e-06, 8.17428736e-05, 7.50365201e-04], [1.00062641e-06, 1.24435348e-04, 8.18357163e-04], ... [1.41367852e-03, 1.79877831e-03, 1.71377081e-02], [1.37466111e-03, 2.30275956e-03, 3.32856625e-02], [1.34840910e-03, 3.03058885e-03, 4.47302125e-02], [1.38495094e-03, 4.78022080e-03, 5.33157960e-02], [1.47192599e-03, 9.51274391e-03, 5.98784201e-02], [1.55525003e-03, 1.41898468e-02, 6.55668080e-02], [1.60700711e-03, 1.95938740e-02, 7.16438368e-02], [1.77496276e-03, 2.32385416e-02, 7.43482858e-02], [1.91565300e-03, 2.69467719e-02, 7.60267600e-02], [1.93555490e-03, 2.79643107e-02, 7.59711117e-02], [2.08756095e-03, 2.94796471e-02, 7.63885081e-02], [2.20756372e-03, 3.05341445e-02, 7.48512000e-02], [2.07593641e-03, 3.59162269e-03, 2.28384528e-02], [2.05276557e-03, 1.21652649e-03, 2.78259465e-03], [2.36642803e-03, 1.12493162e-03, 2.53933412e-03], [2.31716968e-03, 1.69391045e-03, 1.25961495e-03], [2.22260458e-03, 1.90275419e-03, 6.10022515e-04], [2.06597475e-03, 1.90889882e-03, 6.11530559e-04], [1.90941000e-03, 1.74662611e-03, 4.70483530e-04], [1.74895767e-03, 1.62311806e-03, 3.52516159e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float322.751e-08 1.009e-07 ... 2.409e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[2.75141758e-08, 1.00927956e-07, 1.91842332e-07], [2.76572063e-08, 1.02083213e-07, 1.81103673e-07], [2.84073831e-08, 1.02706849e-07, 1.68768793e-07], [2.92201214e-08, 1.00639696e-07, 1.60052934e-07], [2.93619955e-08, 9.66570397e-08, 1.54343581e-07], [2.81556733e-08, 9.44912699e-08, 1.47770351e-07], [2.80691328e-08, 9.18277721e-08, 1.33315652e-07], [2.70703797e-08, 8.95299763e-08, 1.31178382e-07], [2.80460561e-08, 8.83356108e-08, 1.22648729e-07], [2.98051575e-08, 8.39754293e-08, 1.20592745e-07], [3.04549310e-08, 8.25981274e-08, 1.13883360e-07], [3.01034575e-08, 8.12708763e-08, 1.13250536e-07], [3.03417202e-08, 7.92105439e-08, 1.12164003e-07], [2.80577197e-08, 7.73159456e-08, 1.08730454e-07], [2.65831268e-08, 7.54789724e-08, 1.04768517e-07], [2.50681502e-08, 7.38985193e-08, 1.02204666e-07], [2.32162165e-08, 7.19843598e-08, 1.22872251e-07], [2.16796110e-08, 7.61822463e-08, 1.42449750e-07], [1.97460412e-08, 7.91168873e-08, 1.52065113e-07], [1.99246042e-08, 8.23590369e-08, 1.69472955e-07], ... [8.87343390e-08, 1.92970333e-07, 3.78641403e-07], [9.27110761e-08, 3.28967673e-07, 4.08734365e-07], [9.84670478e-08, 3.96731309e-07, 3.57619427e-07], [1.05839803e-07, 4.50980082e-07, 2.93132842e-07], [1.18670741e-07, 4.21865536e-07, 2.44616189e-07], [1.36195439e-07, 3.84792088e-07, 2.07911881e-07], [1.57122187e-07, 3.32016810e-07, 1.79250890e-07], [1.79287312e-07, 2.67496006e-07, 1.59683267e-07], [1.84715390e-07, 2.25708632e-07, 1.48024810e-07], [1.82531465e-07, 2.04892004e-07, 1.40819395e-07], [1.78498823e-07, 1.90133846e-07, 1.34555791e-07], [1.77062404e-07, 1.72623118e-07, 1.28240089e-07], [1.66876461e-07, 7.90816728e-08, 5.31014024e-08], [1.57211332e-07, 6.07850836e-08, 1.32667282e-08], [1.78480164e-07, 8.18638455e-08, 1.54134518e-08], [1.72224148e-07, 9.17385847e-08, 1.27861615e-08], [1.62150997e-07, 9.55862518e-08, 1.27261250e-08], [1.47029624e-07, 8.91621710e-08, 1.51694479e-08], [1.33136325e-07, 8.16399606e-08, 1.85937061e-08], [1.20080372e-07, 7.40396757e-08, 2.40899496e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float326.284e-08 2.881e-07 ... 6.942e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[6.28427301e-08, 2.88068264e-07, 8.26305950e-07], [6.27368166e-08, 2.98960629e-07, 8.71046439e-07], [6.44347011e-08, 3.12472736e-07, 9.56297868e-07], [6.58234427e-08, 2.90687467e-07, 8.94805169e-07], [6.38682138e-08, 2.69036633e-07, 8.48512173e-07], [6.23498408e-08, 2.60920700e-07, 7.95632900e-07], [6.17878939e-08, 2.35277298e-07, 7.43213093e-07], [6.09875315e-08, 2.30552388e-07, 7.15444685e-07], [6.19845935e-08, 2.33280574e-07, 6.38697657e-07], [6.30164294e-08, 2.23609277e-07, 6.13384714e-07], [6.33949782e-08, 2.18304905e-07, 6.51640221e-07], [6.44858815e-08, 2.16931255e-07, 6.27009797e-07], [6.66231301e-08, 2.17100066e-07, 5.76267610e-07], [6.13616891e-08, 2.07990112e-07, 5.76540401e-07], [5.89936313e-08, 1.95030083e-07, 5.98523343e-07], [5.78152637e-08, 1.80715574e-07, 5.96839300e-07], [5.59458790e-08, 1.73955854e-07, 6.28465671e-07], [5.44911316e-08, 1.69204824e-07, 6.99216798e-07], [5.31692095e-08, 1.68147437e-07, 7.60493265e-07], [5.20742489e-08, 1.85152899e-07, 7.71945395e-07], ... [8.01801932e-08, 1.43546359e-07, 3.93581416e-07], [8.15358305e-08, 2.63959834e-07, 4.96796190e-07], [8.34458191e-08, 3.63753145e-07, 3.89451685e-07], [9.06807145e-08, 3.98819338e-07, 2.63717595e-07], [9.65297176e-08, 3.69848379e-07, 1.95779862e-07], [1.16798915e-07, 3.11457910e-07, 1.47851480e-07], [1.31431392e-07, 2.54156788e-07, 1.17898125e-07], [1.45972948e-07, 2.09105082e-07, 9.08319109e-08], [1.43596168e-07, 1.64574814e-07, 7.50877760e-08], [1.30332225e-07, 1.36982720e-07, 7.11929431e-08], [1.25370548e-07, 1.19903476e-07, 6.65046258e-08], [1.23980243e-07, 1.06661567e-07, 6.42934452e-08], [1.02193404e-07, 1.57689470e-08, 2.12460556e-08], [1.21662225e-07, 7.79856713e-09, 2.85935986e-09], [1.81704664e-07, 1.52409090e-08, 4.98988229e-09], [1.96759984e-07, 4.02906437e-08, 3.78793130e-09], [1.93130234e-07, 6.06322743e-08, 3.38508843e-09], [1.76792256e-07, 6.97176006e-08, 4.78815654e-09], [1.61031181e-07, 6.62385702e-08, 5.76574166e-09], [1.42380628e-07, 6.12754292e-08, 6.94171742e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-5.201e-10 ... -8.505e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[-5.20121723e-10, -9.14844023e-09, -4.93047665e-08], [-5.22739962e-10, -9.68542224e-09, -5.20738865e-08], [-5.26788724e-10, -1.03196767e-08, -5.22399120e-08], [-5.31486521e-10, -9.74224612e-09, -5.16224858e-08], [-5.28820432e-10, -8.79323636e-09, -4.80655693e-08], [-5.22581700e-10, -7.71664954e-09, -4.50965629e-08], [-5.21203691e-10, -5.64962477e-09, -4.01015399e-08], [-5.18142307e-10, -5.64664893e-09, -3.91272224e-08], [-5.23529831e-10, -5.48414247e-09, -3.74587685e-08], [-5.28541544e-10, -5.04714226e-09, -3.46567788e-08], [-5.26243826e-10, -4.30064651e-09, -3.66826640e-08], [-5.30970545e-10, -4.58714178e-09, -3.40342439e-08], [-5.40062717e-10, -4.76399675e-09, -3.22511404e-08], [-5.22477839e-10, -3.94990529e-09, -3.11737196e-08], [-5.14006226e-10, -2.94821079e-09, -3.12277812e-08], [-5.08801501e-10, -2.20570762e-09, -3.06338883e-08], [-5.04617959e-10, -1.54705970e-09, -3.28621717e-08], [-4.98949326e-10, -1.16123022e-09, -3.47503857e-08], [-4.88723784e-10, -1.06063713e-09, -3.58807526e-08], [-4.87477392e-10, -2.45552156e-09, -3.57378731e-08], ... [-1.19211130e-08, -2.91519573e-08, -1.85755169e-07], [-1.16928520e-08, -4.86725327e-08, -1.75252964e-07], [-1.28964057e-08, -7.18805637e-08, -1.54877227e-07], [-1.38640353e-08, -8.52027426e-08, -1.34078221e-07], [-1.57190652e-08, -8.47513775e-08, -1.16542580e-07], [-1.61323648e-08, -8.39835579e-08, -1.00404989e-07], [-1.95944843e-08, -7.88693768e-08, -8.40583709e-08], [-1.87968698e-08, -7.23438234e-08, -7.34735437e-08], [-1.98578043e-08, -6.28177261e-08, -6.60879067e-08], [-1.82452986e-08, -6.05719066e-08, -6.59063346e-08], [-2.02872936e-08, -5.50794574e-08, -6.34640145e-08], [-1.40410705e-08, -6.71363454e-09, -1.99716030e-08], [-1.54548694e-08, -1.65583369e-09, -1.90645566e-09], [-2.33801369e-08, -1.92635197e-09, -2.99118907e-09], [-2.59499373e-08, -5.00197261e-09, -1.58494307e-09], [-2.67266209e-08, -8.41553582e-09, -7.80392917e-10], [-2.56543515e-08, -9.63710711e-09, -8.30871316e-10], [-2.25431442e-08, -9.36158528e-09, -7.23190507e-10], [-2.03528803e-08, -8.93805474e-09, -8.50497450e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.8128 -35.11 ... -31.0 -4.416
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.81275251, -35.10540508, -107.92516258], [ -0.81452745, -37.30334659, -111.43608918], [ -0.81740106, -38.24310327, -110.81864896], [ -0.8337643 , -36.90952601, -106.07761669], [ -0.83078597, -33.39633727, -102.65993507], [ -0.81278874, -31.48373516, -96.20815795], [ -0.80897951, -29.27580381, -91.31173044], [ -0.80735026, -28.07736327, -86.57831432], [ -0.81581746, -28.11210656, -80.24805898], [ -0.8243209 , -26.31316743, -79.05538436], [ -0.82850863, -24.78306735, -78.83334196], [ -0.83782546, -24.51323225, -76.66792171], [ -0.84537657, -25.62885892, -73.43640519], [ -0.81986974, -22.42427483, -71.26076615], [ -0.79646231, -19.5188711 , -69.76079995], [ -0.78984496, -16.53241175, -67.13581603], [ -0.77231664, -13.41119337, -74.19449757], [ -0.7643023 , -12.92565907, -79.32650121], [ -0.75232554, -11.57467935, -82.422927 ], [ -0.7441352 , -17.67938361, -86.26225594], ... [ -31.87862027, -44.16697882, -261.96079925], [ -31.56887868, -72.83843194, -377.9896837 ], [ -30.9545401 , -128.39360995, -375.99278148], [ -31.56165977, -188.84136826, -353.71039164], [ -35.07868203, -213.64488296, -324.67284683], [ -38.16875777, -216.86511225, -295.0096038 ], [ -40.91438287, -214.01366732, -270.30288383], [ -47.57095843, -204.76566038, -247.3864047 ], [ -51.89456633, -194.27813913, -231.64378862], [ -53.58953487, -177.50533146, -221.23058939], [ -53.27081906, -171.74451817, -210.98684748], [ -57.0413013 , -164.76087878, -205.40908003], [ -40.80732238, -27.55403688, -71.54460267], [ -44.89828358, -8.20514559, -9.65387598], [ -59.21442771, -10.90296723, -11.91643733], [ -59.91662008, -24.30769411, -7.67025163], [ -60.46739161, -32.15060793, -4.7937284 ], [ -55.39888652, -34.30766255, -5.44876747], [ -51.89013205, -32.49015759, -5.0439275 ], [ -46.88159532, -31.00173399, -4.41609556]]]) - S2(depth, hour, tau_bins)float320.0002157 0.0002126 ... 2.707e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[2.15672277e-04, 2.12571860e-04, 2.09773949e-04], [2.11442632e-04, 2.13960826e-04, 2.08297279e-04], [2.12287967e-04, 2.13467691e-04, 2.08366328e-04], [2.10965911e-04, 2.11972176e-04, 2.07678633e-04], [2.12011786e-04, 2.13226915e-04, 2.05537624e-04], [2.10400962e-04, 2.13552674e-04, 2.04386102e-04], [2.12897561e-04, 2.12111685e-04, 2.02022580e-04], [2.10545331e-04, 2.11847306e-04, 1.97839865e-04], [2.11565115e-04, 2.10612125e-04, 1.97103727e-04], [2.16023400e-04, 2.07201680e-04, 1.95945642e-04], [2.16643792e-04, 2.06816840e-04, 1.92040781e-04], [2.15596141e-04, 2.06519107e-04, 1.87373429e-04], [2.16904009e-04, 2.03615797e-04, 1.89063925e-04], [2.13125197e-04, 2.04004653e-04, 1.98501541e-04], [2.10339669e-04, 2.04935292e-04, 1.98550842e-04], [2.10460654e-04, 2.03726391e-04, 2.05102784e-04], [2.10142141e-04, 2.03563191e-04, 2.09907332e-04], [2.13177133e-04, 2.04036303e-04, 2.12833940e-04], [2.09707534e-04, 2.06934783e-04, 2.16156070e-04], [2.09469115e-04, 2.09337260e-04, 2.18377172e-04], ... [7.56139125e-05, 8.71341690e-05, 4.80451272e-05], [7.91544371e-05, 1.01554950e-04, 2.81878711e-05], [8.30380959e-05, 1.09442073e-04, 1.88173381e-05], [8.69750729e-05, 1.01881058e-04, 1.30142435e-05], [9.41247126e-05, 7.86011806e-05, 9.69898065e-06], [9.96056042e-05, 5.49317556e-05, 7.44869567e-06], [1.06645661e-04, 3.67982102e-05, 5.92534889e-06], [1.08007203e-04, 2.58306009e-05, 4.90106504e-06], [1.06873224e-04, 1.89379862e-05, 4.29951342e-06], [1.06376858e-04, 1.58985531e-05, 4.05997798e-06], [1.01732832e-04, 1.36065619e-05, 3.86104784e-06], [1.00730147e-04, 1.22374022e-05, 3.82177495e-06], [1.14109302e-04, 2.29600610e-05, 3.37873757e-06], [1.07913307e-04, 4.26053302e-05, 4.52991026e-06], [1.08090324e-04, 5.52821584e-05, 5.62377090e-06], [1.04775114e-04, 5.55655897e-05, 7.24206393e-06], [1.01420417e-04, 5.40483234e-05, 1.19902697e-05], [9.72153139e-05, 5.22391856e-05, 1.74126035e-05], [9.16627687e-05, 5.07932564e-05, 2.12584673e-05], [8.66877963e-05, 5.03534393e-05, 2.70670953e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002498 0.0001501 ... 1.074e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.49770761e-04, 1.50098756e-04, 1.11120258e-04], [2.47220974e-04, 1.48095016e-04, 1.09469358e-04], [2.45606410e-04, 1.47212602e-04, 1.16273659e-04], [2.44910567e-04, 1.46733626e-04, 1.18762495e-04], [2.46512733e-04, 1.49197745e-04, 1.12651309e-04], [2.48484663e-04, 1.50325548e-04, 1.12898553e-04], [2.50331883e-04, 1.52102992e-04, 1.12781214e-04], [2.47574528e-04, 1.54107634e-04, 1.07344546e-04], [2.44933646e-04, 1.54900685e-04, 1.09973000e-04], [2.42529481e-04, 1.55066184e-04, 1.11506415e-04], [2.43119634e-04, 1.55268455e-04, 1.09090055e-04], [2.40484907e-04, 1.56507798e-04, 1.07217173e-04], [2.42932118e-04, 1.54143461e-04, 1.09352390e-04], [2.47160351e-04, 1.55096990e-04, 1.11676884e-04], [2.48186872e-04, 1.56149719e-04, 1.17096948e-04], [2.47517542e-04, 1.57528339e-04, 1.21044686e-04], [2.51487305e-04, 1.58637442e-04, 1.20155579e-04], [2.54361890e-04, 1.61505086e-04, 1.20364450e-04], [2.58087355e-04, 1.62304452e-04, 1.22957426e-04], [2.59101274e-04, 1.60314288e-04, 1.22088502e-04], ... [2.85044662e-05, 2.38834564e-05, 1.88644735e-05], [2.91151373e-05, 2.64380724e-05, 1.56137939e-05], [2.97974948e-05, 2.77846120e-05, 1.16638857e-05], [3.07708033e-05, 2.75006969e-05, 8.30140198e-06], [3.24869034e-05, 2.40745630e-05, 6.47047591e-06], [3.33005883e-05, 1.96775745e-05, 5.33579077e-06], [3.45363878e-05, 1.50565565e-05, 4.39064070e-06], [3.53112300e-05, 1.14167287e-05, 3.83091219e-06], [3.45137305e-05, 8.98336566e-06, 3.45510307e-06], [3.41937484e-05, 7.86413875e-06, 3.39021835e-06], [3.30625990e-05, 7.16474597e-06, 3.19372793e-06], [3.16721853e-05, 6.60015530e-06, 3.07497498e-06], [3.25626825e-05, 6.08912433e-06, 2.91151355e-06], [3.47301902e-05, 7.46536080e-06, 2.89518221e-06], [3.47736095e-05, 1.20941550e-05, 3.61292769e-06], [3.44612490e-05, 1.47121618e-05, 4.20345714e-06], [3.39114413e-05, 1.61574008e-05, 5.35977597e-06], [3.25952788e-05, 1.72388645e-05, 7.28179202e-06], [3.15559228e-05, 1.78157061e-05, 8.98511098e-06], [3.07502341e-05, 1.84755536e-05, 1.07377618e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float320.6921 0.535 ... 0.4047 0.4431
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[0.6920738 , 0.5349543 , 0.4580132 ], [0.6879399 , 0.5314694 , 0.4592346 ], [0.6806804 , 0.5308178 , 0.4657764 ], [0.6791985 , 0.53030324, 0.47593078], [0.6806549 , 0.5339809 , 0.4767241 ], [0.68695664, 0.53564453, 0.48169115], [0.6896516 , 0.53775823, 0.49365795], [0.6888744 , 0.5401193 , 0.49436718], [0.68251336, 0.5424917 , 0.5011655 ], [0.68021417, 0.54622084, 0.5020577 ], [0.6766766 , 0.5488351 , 0.5055933 ], [0.6775784 , 0.55120665, 0.5052763 ], [0.6728198 , 0.55457515, 0.5086805 ], [0.6839738 , 0.55721325, 0.51228535], [0.691461 , 0.5597189 , 0.5188104 ], [0.69568074, 0.56400734, 0.52135473], [0.7045409 , 0.56998813, 0.5013685 ], [0.7121001 , 0.56713057, 0.49566406], [0.72767735, 0.56552804, 0.490233 ], [0.7288958 , 0.5595642 , 0.47649854], ... [0.40634552, 0.33188626, 0.5003445 ], [0.39810854, 0.3208425 , 0.581831 ], [0.39363727, 0.32485807, 0.6355092 ], [0.38015234, 0.34808362, 0.66806245], [0.36744684, 0.39145422, 0.6928216 ], [0.35197896, 0.42392755, 0.7202125 ], [0.34439027, 0.46392506, 0.74636894], [0.34399074, 0.4974593 , 0.77452666], [0.3449704 , 0.5331218 , 0.7831469 ], [0.34179208, 0.54510725, 0.80061936], [0.34201652, 0.5604679 , 0.8105681 ], [0.34889582, 0.5762068 , 0.81085813], [0.2864733 , 0.38954324, 0.84244406], [0.31387162, 0.28162244, 0.6502222 ], [0.32518497, 0.30333692, 0.62802637], [0.3409173 , 0.334327 , 0.56850284], [0.3572793 , 0.35580266, 0.46713182], [0.36969233, 0.37518197, 0.453887 ], [0.37960896, 0.39090776, 0.44790077], [0.39049977, 0.40465185, 0.44312316]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float320.6525 0.4943 ... 0.3079 0.3627
- long_name :
- $Ri^g_T$
array([[[0.6524795 , 0.49431792, 0.40401042], [0.6476281 , 0.49186328, 0.40452516], [0.6441446 , 0.4895203 , 0.4143563 ], [0.64207554, 0.49051276, 0.41974872], [0.6392752 , 0.49562863, 0.4259835 ], [0.6485602 , 0.49845082, 0.43141162], [0.65098137, 0.50073636, 0.43876797], [0.6474688 , 0.50299937, 0.4344629 ], [0.64145947, 0.50219667, 0.44302076], [0.63325673, 0.5045558 , 0.44308442], [0.6337309 , 0.50653815, 0.44803062], [0.63019466, 0.5095291 , 0.45472556], [0.630605 , 0.50949144, 0.4569693 ], [0.6359448 , 0.51431185, 0.46192995], [0.64430714, 0.5192264 , 0.472704 ], [0.65253395, 0.52388585, 0.47870392], [0.6600066 , 0.52851456, 0.4612955 ], [0.6755862 , 0.5250453 , 0.45920655], [0.68343335, 0.52443343, 0.45433536], [0.6843606 , 0.52000076, 0.43686157], ... [0.29926416, 0.23857142, 0.26230258], [0.2974115 , 0.21804348, 0.30031466], [0.28810856, 0.21146052, 0.32518813], [0.28323996, 0.21127589, 0.3577328 ], [0.26677144, 0.2186438 , 0.38158107], [0.2535754 , 0.23513977, 0.39806974], [0.24205409, 0.25546497, 0.40978163], [0.2332109 , 0.2754921 , 0.42806375], [0.22958612, 0.2987565 , 0.43636626], [0.23360637, 0.306973 , 0.4462604 ], [0.23348331, 0.31307265, 0.45968074], [0.23676656, 0.32316506, 0.46267766], [0.22447279, 0.25387365, 0.5430862 ], [0.24416187, 0.2449134 , 0.4584749 ], [0.2532297 , 0.24663666, 0.46583867], [0.26291543, 0.25914267, 0.46689865], [0.27117586, 0.27355063, 0.43329036], [0.28200358, 0.28652632, 0.39690372], [0.28587258, 0.29844803, 0.38097277], [0.29391518, 0.30785322, 0.36271706]]], dtype=float32) - tau(hour, tau_bins)float320.02913 0.05397 ... 0.05443 0.08317
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02912814, 0.05397057, 0.08243489], [0.02951978, 0.05395206, 0.08258021], [0.02955842, 0.0539168 , 0.08289316], [0.02935282, 0.05389314, 0.08354414], [0.02921309, 0.05400709, 0.083749 ], [0.02929978, 0.05425711, 0.0844179 ], [0.02939584, 0.05473844, 0.08448129], [0.02932024, 0.05432779, 0.08426537], [0.02936672, 0.05443417, 0.08411882], [0.02933902, 0.05419946, 0.08512807], [0.0292527 , 0.05430375, 0.08504955], [0.02921882, 0.05419347, 0.08442991], [0.02883983, 0.05426022, 0.08442935], [0.02913588, 0.05487758, 0.08464634], [0.02929619, 0.05555559, 0.08535865], [0.02973468, 0.0561522 , 0.08564808], [0.02978581, 0.05643295, 0.08602639], [0.0299926 , 0.05669548, 0.08611111], [0.0300919 , 0.05693631, 0.08645762], [0.02996605, 0.05639978, 0.08547585], [0.02971533, 0.05582306, 0.08512087], [0.02977535, 0.05557946, 0.08559605], [0.0296651 , 0.05494839, 0.08416149], [0.02947801, 0.05443047, 0.08317043]], dtype=float32)
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 0.000262 ... 0.0003525 eps (depth, hour, tau_bins) float32 2.751e-08 1.009e-07 ... 2.409e-08 chi (depth, hour, tau_bins) float32 6.284e-08 2.881e-07 ... 6.942e-09 Jb (depth, hour, tau_bins) float32 -5.201e-10 ... -8.505e-10 Jq (depth, hour, tau_bins) float64 -0.8128 -35.11 ... -31.0 -4.416 S2 (depth, hour, tau_bins) float32 0.0002157 0.0002126 ... 2.707e-05 N2 (depth, hour, tau_bins) float32 0.0002498 0.0001501 ... 1.074e-05 Rig (depth, hour, tau_bins) float32 0.6921 0.535 ... 0.4047 0.4431 Rig_T (depth, hour, tau_bins) float32 0.6525 0.4943 ... 0.3079 0.3627 tau (hour, tau_bins) float32 0.02913 0.05397 ... 0.05443 0.08317 Attributes: title: KD=0, KV=0new_baseline.hb- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0001793
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006263e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], ... [9.4132446e-04, 1.0125230e-03, 3.3146515e-03], [9.1822050e-04, 1.1766625e-03, 1.4005989e-02], [9.2203892e-04, 1.2403573e-03, 2.3555636e-02], [9.2468399e-04, 1.4708596e-03, 3.6420863e-02], [9.4251416e-04, 1.7338136e-03, 4.3797132e-02], [9.6162333e-04, 3.1932166e-03, 4.8042994e-02], [9.9941925e-04, 6.7671593e-03, 5.3763784e-02], [1.0284228e-03, 1.0356332e-02, 5.6218788e-02], [1.0461721e-03, 1.3733745e-02, 5.8365785e-02], [1.0795848e-03, 1.4616154e-02, 5.8618966e-02], [1.1059251e-03, 1.5877362e-02, 5.8043797e-02], [1.1142871e-03, 1.6551722e-02, 5.5786945e-02], [1.2179909e-03, 1.7418606e-03, 1.4232323e-02], [1.2699188e-03, 1.2363050e-03, 6.4157473e-04], [1.3006497e-03, 1.2935506e-03, 7.0208183e-04], [1.2927618e-03, 1.3108684e-03, 2.6574192e-04], [1.2749400e-03, 1.2415671e-03, 2.4541048e-04], [1.2317502e-03, 1.1815999e-03, 2.0568147e-04], [1.1754853e-03, 1.1246946e-03, 2.0106597e-04], [1.1240497e-03, 1.0682463e-03, 1.7925282e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float321.167e-10 1.08e-09 ... 1.188e-08
- long_name :
- $SP$
- units :
- W/kg
array([[[1.16701815e-10, 1.08015230e-09, 5.63116132e-09], [1.19111165e-10, 1.07898579e-09, 7.99824651e-09], [1.18361237e-10, 1.08791587e-09, 1.18005001e-08], [1.27412164e-10, 1.05052633e-09, 1.31663205e-08], [1.17635207e-10, 1.03097231e-09, 1.75062738e-08], [9.73851971e-11, 1.02187192e-09, 1.88077038e-08], [8.75817960e-11, 9.82036785e-10, 1.35005802e-08], [9.26381610e-11, 9.65200475e-10, 2.44190765e-08], [9.95368094e-11, 9.58345070e-10, 2.83898807e-08], [1.10836319e-10, 9.62123381e-10, 2.31838033e-08], [1.10051565e-10, 9.44794021e-10, 2.89860242e-08], [1.05334921e-10, 9.44809120e-10, 2.77833383e-08], [1.03289294e-10, 9.67400937e-10, 1.99497752e-08], [9.65807712e-11, 9.38989664e-10, 1.21494139e-08], [9.11579978e-11, 8.93369712e-10, 1.11879901e-08], [7.61567059e-11, 8.27433844e-10, 6.17140605e-09], [6.14191406e-11, 7.72974462e-10, 7.20225124e-09], [5.00269270e-11, 7.47057416e-10, 8.19066770e-09], [4.44424011e-11, 7.16618154e-10, 5.89383209e-09], [4.85056093e-11, 7.63983099e-10, 1.02767244e-08], ... [1.28741050e-07, 1.87361209e-07, 6.15806414e-07], [1.28145004e-07, 2.21985459e-07, 6.48607397e-07], [1.27098886e-07, 3.49445969e-07, 6.69406347e-07], [1.32843809e-07, 4.85158864e-07, 5.94786627e-07], [1.43734553e-07, 5.69935992e-07, 4.71317179e-07], [1.57610160e-07, 5.92521985e-07, 4.12530767e-07], [1.75905740e-07, 5.66274707e-07, 3.34720511e-07], [2.00085310e-07, 5.31975843e-07, 2.84828332e-07], [2.20968275e-07, 4.93082723e-07, 2.51338349e-07], [2.30489633e-07, 4.53310889e-07, 2.24856095e-07], [2.29500998e-07, 4.13383020e-07, 2.13616687e-07], [2.36499659e-07, 3.86044064e-07, 2.03071892e-07], [2.24949005e-07, 1.76399539e-07, 8.07398237e-08], [2.27280538e-07, 1.37437539e-07, 1.52178323e-08], [2.22406385e-07, 1.60410394e-07, 2.28007764e-08], [2.22824241e-07, 1.65306176e-07, 1.45026267e-08], [2.12226539e-07, 1.50751220e-07, 1.26914417e-08], [1.97863045e-07, 1.31234714e-07, 1.23363186e-08], [1.87246343e-07, 1.16189248e-07, 1.27463036e-08], [1.74075794e-07, 1.08521846e-07, 1.18753531e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float321.739e-08 2.265e-08 ... 2.921e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[1.73884356e-08, 2.26525483e-08, 2.44861411e-08], [1.74541945e-08, 2.25107541e-08, 2.62088093e-08], [1.74165553e-08, 2.30953781e-08, 2.58756945e-08], [1.76193122e-08, 2.29807107e-08, 2.62177746e-08], [1.75198185e-08, 2.28707417e-08, 2.60189026e-08], [1.77797759e-08, 2.27358683e-08, 2.63329873e-08], [1.77949939e-08, 2.27717489e-08, 2.54955133e-08], [1.77553900e-08, 2.28247217e-08, 2.53485677e-08], [1.76991506e-08, 2.27228103e-08, 2.53738861e-08], [1.76896275e-08, 2.26282602e-08, 2.46510261e-08], [1.72673751e-08, 2.28223289e-08, 2.49068179e-08], [1.70976175e-08, 2.28065726e-08, 2.59466173e-08], [1.74429111e-08, 2.25265229e-08, 2.53189221e-08], [1.71922050e-08, 2.24622845e-08, 2.49400109e-08], [1.72470962e-08, 2.21477912e-08, 2.48894487e-08], [1.71766423e-08, 2.19773977e-08, 2.52571652e-08], [1.71460073e-08, 2.18138965e-08, 2.48438727e-08], [1.68117325e-08, 2.15464286e-08, 2.56811177e-08], [1.70101799e-08, 2.12656666e-08, 2.53530850e-08], [1.67263465e-08, 2.17167706e-08, 2.57135699e-08], ... [1.75568431e-07, 1.69639179e-07, 3.88403151e-07], [1.75191545e-07, 1.87313177e-07, 5.02682212e-07], [1.78678505e-07, 2.76781094e-07, 4.66718802e-07], [1.79366026e-07, 3.55949112e-07, 4.29998522e-07], [1.93214674e-07, 4.01829709e-07, 3.54981921e-07], [2.06669668e-07, 3.73105195e-07, 2.72762577e-07], [2.30700508e-07, 3.66185759e-07, 2.02141905e-07], [2.37789180e-07, 3.12547570e-07, 1.59825987e-07], [2.32979346e-07, 2.58506304e-07, 1.35901701e-07], [2.31390715e-07, 2.30672811e-07, 1.19482976e-07], [2.24134709e-07, 2.02506129e-07, 1.22481609e-07], [2.03919456e-07, 1.81497555e-07, 1.22820964e-07], [2.27858976e-07, 2.54538524e-08, 2.69130194e-08], [2.34801988e-07, 3.19046833e-08, 1.72959358e-09], [2.60971547e-07, 7.96455524e-08, 3.60839358e-09], [2.78977524e-07, 9.71027134e-08, 2.21884200e-09], [2.79261258e-07, 9.98343097e-08, 2.69382872e-09], [2.76931189e-07, 1.00610364e-07, 2.64924727e-09], [2.61743025e-07, 9.72737908e-08, 2.82410162e-09], [2.45267103e-07, 9.24204926e-08, 2.92052182e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-2.555e-10 ... -2.291e-11
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[-2.55470034e-10, -2.91401792e-10, -3.19249349e-10], [-2.56496713e-10, -2.91035779e-10, -3.23981342e-10], [-2.53829818e-10, -2.94654967e-10, -3.14947873e-10], [-2.55533705e-10, -2.92805835e-10, -3.09960974e-10], [-2.56380861e-10, -2.91983326e-10, -3.10544312e-10], [-2.56754840e-10, -2.91529800e-10, -3.07330911e-10], [-2.56076688e-10, -2.91074637e-10, -3.00592301e-10], [-2.54780752e-10, -2.91799696e-10, -3.05651116e-10], [-2.57439070e-10, -2.91535185e-10, -3.07487813e-10], [-2.56943383e-10, -2.93832736e-10, -3.00166780e-10], [-2.55828830e-10, -2.93334773e-10, -3.05036496e-10], [-2.54726990e-10, -2.92834201e-10, -3.05739545e-10], [-2.56902666e-10, -2.92254110e-10, -3.07121273e-10], [-2.54530674e-10, -2.91644903e-10, -2.97666614e-10], [-2.55004962e-10, -2.89583413e-10, -3.04066772e-10], [-2.52027094e-10, -2.88339130e-10, -3.08847586e-10], [-2.50817089e-10, -2.87278257e-10, -3.02292469e-10], [-2.49159637e-10, -2.86964563e-10, -3.07309900e-10], [-2.51137861e-10, -2.85814983e-10, -3.09010206e-10], [-2.47247639e-10, -2.86292767e-10, -3.14035603e-10], ... [-1.65704819e-08, -1.85746529e-08, -1.38406989e-07], [-1.66631597e-08, -2.50191388e-08, -1.54171133e-07], [-1.78619324e-08, -3.59505563e-08, -1.83110956e-07], [-1.81895565e-08, -5.13915772e-08, -1.60862925e-07], [-1.90937630e-08, -6.16821083e-08, -1.43145357e-07], [-2.18083027e-08, -7.16589668e-08, -1.28102954e-07], [-2.29776802e-08, -7.47350626e-08, -1.13467706e-07], [-2.24974031e-08, -7.14504864e-08, -1.11496405e-07], [-2.32104789e-08, -6.69645033e-08, -1.04462508e-07], [-2.28019950e-08, -6.31832933e-08, -1.03584043e-07], [-2.19223555e-08, -6.01868848e-08, -1.01363128e-07], [-2.10841904e-08, -9.55064028e-09, -2.09327062e-08], [-2.02724735e-08, -3.44046680e-09, -6.70337619e-10], [-2.20876579e-08, -6.45958576e-09, -1.19146870e-09], [-2.26566019e-08, -9.40468858e-09, -4.07481243e-10], [-2.29477024e-08, -9.73539294e-09, -2.40757192e-10], [-2.31211477e-08, -9.75570824e-09, -1.00751248e-10], [-2.25524239e-08, -9.54282964e-09, -8.35792408e-11], [-2.11913314e-08, -9.55753965e-09, -2.29118079e-11]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.4038 -0.4617 ... -30.73 -1.899
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.40383201, -0.46170693, -0.47979607], [ -0.40629385, -0.46081924, -0.49478992], [ -0.40548916, -0.46611381, -0.48753303], [ -0.40707212, -0.46448855, -0.49537153], [ -0.40654759, -0.46543408, -0.49638149], [ -0.40767024, -0.46317764, -0.5001483 ], [ -0.40825707, -0.46347099, -0.48866422], [ -0.40965568, -0.46438217, -0.48843255], [ -0.40948291, -0.46580355, -0.48770104], [ -0.40507042, -0.46631911, -0.48339054], [ -0.40664369, -0.46244279, -0.48619947], [ -0.39999109, -0.46313942, -0.49600014], [ -0.40572711, -0.46118047, -0.48885789], [ -0.40192261, -0.46193756, -0.47936888], [ -0.40096163, -0.45710388, -0.48437711], [ -0.39976277, -0.45286841, -0.4927702 ], [ -0.40124605, -0.452229 , -0.48249731], [ -0.39830072, -0.45048057, -0.49243466], [ -0.40160454, -0.44609005, -0.4880124 ], [ -0.39683667, -0.45253467, -0.49126503], ... [ -38.01416094, -37.71273213, -146.00726222], [ -38.08362599, -42.7750946 , -298.59354522], [ -37.79889323, -56.78291587, -339.93163012], [ -38.22386721, -86.86501178, -375.49648732], [ -39.24619021, -127.04311749, -336.79308851], [ -41.1660171 , -157.00324937, -323.79881367], [ -44.60286042, -176.94763065, -294.54638861], [ -48.23935169, -180.63089509, -263.29843897], [ -49.37562729, -176.74255824, -248.19471826], [ -52.69931838, -165.18010348, -235.23162031], [ -53.43977151, -160.63160397, -234.23988518], [ -53.31057096, -152.06274918, -227.99312568], [ -49.36203681, -33.31373377, -62.13097584], [ -50.61750746, -17.60716132, -4.09462887], [ -53.39291811, -30.18945973, -5.85316103], [ -55.27374453, -34.94254046, -2.91233767], [ -55.69251915, -34.25285055, -2.41855112], [ -54.34907925, -32.83374329, -2.14437555], [ -52.16474975, -31.96696915, -2.31426363], [ -48.46496735, -30.72726698, -1.89948483]]]) - S2(depth, hour, tau_bins)float326.214e-05 0.0002147 ... 3.608e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[6.2135194e-05, 2.1472209e-04, 3.2808020e-04], [6.2408239e-05, 2.1617881e-04, 3.8527348e-04], [6.0956540e-05, 2.2225048e-04, 3.8728234e-04], [6.1642189e-05, 2.1964675e-04, 4.0369909e-04], [5.8844664e-05, 2.1374767e-04, 4.1417207e-04], [5.7150966e-05, 2.0920373e-04, 4.1820243e-04], [5.5285251e-05, 2.0723182e-04, 4.0379536e-04], [5.5278397e-05, 2.0315332e-04, 4.3007903e-04], [5.6005490e-05, 2.0021363e-04, 4.3119671e-04], [5.7905778e-05, 2.0268056e-04, 4.2984835e-04], [5.8969716e-05, 2.0108584e-04, 4.9466669e-04], [5.7197434e-05, 2.0400004e-04, 4.8310720e-04], [5.8316582e-05, 2.0677842e-04, 4.4798007e-04], [5.5217875e-05, 2.0273292e-04, 3.9979507e-04], [5.2473857e-05, 1.9077447e-04, 3.7621343e-04], [5.0210474e-05, 1.8508767e-04, 3.5905137e-04], [4.9514329e-05, 1.7809295e-04, 3.6660919e-04], [4.7522713e-05, 1.7627905e-04, 3.5958833e-04], [4.7432528e-05, 1.7114768e-04, 3.4196809e-04], [4.7061854e-05, 1.7858839e-04, 3.7684879e-04], ... [1.6946094e-04, 1.5064844e-04, 9.2533010e-05], [1.7211071e-04, 1.6139646e-04, 8.4166153e-05], [1.7541717e-04, 1.7660802e-04, 6.0221559e-05], [1.7440240e-04, 1.9544957e-04, 3.2458531e-05], [1.8071404e-04, 1.9552017e-04, 2.2991680e-05], [1.8980163e-04, 1.7540010e-04, 1.7840273e-05], [1.9475482e-04, 1.4813445e-04, 1.3792065e-05], [2.0412008e-04, 1.0925530e-04, 1.1346765e-05], [2.0858954e-04, 7.4563985e-05, 9.1552747e-06], [2.0937597e-04, 6.3926171e-05, 8.1477992e-06], [2.0614298e-04, 5.4879973e-05, 7.9349493e-06], [2.0774821e-04, 4.9834987e-05, 7.8745115e-06], [2.2729186e-04, 1.0725283e-04, 7.3192496e-06], [2.2412286e-04, 1.3586249e-04, 1.1071936e-05], [2.1546050e-04, 1.4760980e-04, 1.6061604e-05], [2.1067094e-04, 1.4497816e-04, 1.9443167e-05], [2.0641519e-04, 1.3815981e-04, 2.6482619e-05], [2.0400202e-04, 1.3395854e-04, 3.0892639e-05], [1.9902988e-04, 1.3089213e-04, 3.5797952e-05], [1.9289140e-04, 1.2865067e-04, 3.6081707e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002396 0.0002716 ... 1.035e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.39610745e-04, 2.71556084e-04, 2.70128832e-04], [2.40454014e-04, 2.70599645e-04, 2.74386490e-04], [2.38844208e-04, 2.72287260e-04, 2.75661034e-04], [2.39268178e-04, 2.72357342e-04, 2.73914455e-04], [2.38136767e-04, 2.70218472e-04, 2.74914317e-04], [2.40638648e-04, 2.65842449e-04, 2.78192281e-04], [2.39180474e-04, 2.64761271e-04, 2.83272122e-04], [2.38557084e-04, 2.67403608e-04, 2.80232227e-04], [2.40423135e-04, 2.67150201e-04, 2.80136708e-04], [2.40707173e-04, 2.69239477e-04, 2.72929028e-04], [2.40857858e-04, 2.67764030e-04, 2.78347288e-04], [2.40084250e-04, 2.69025011e-04, 2.78279331e-04], [2.41775560e-04, 2.68885138e-04, 2.73582235e-04], [2.38815803e-04, 2.68640055e-04, 2.75888189e-04], [2.39419605e-04, 2.66721123e-04, 2.76292703e-04], [2.39614528e-04, 2.65526905e-04, 2.78615538e-04], [2.39612215e-04, 2.63208203e-04, 2.79091299e-04], [2.38741486e-04, 2.62423680e-04, 2.80824257e-04], [2.38855631e-04, 2.61507288e-04, 2.79753702e-04], [2.38068678e-04, 2.64408678e-04, 2.79467757e-04], ... [4.28655549e-05, 3.03763100e-05, 1.97062764e-05], [4.30601140e-05, 3.16175210e-05, 1.90241481e-05], [4.39168252e-05, 3.30525654e-05, 1.80639163e-05], [4.37198396e-05, 3.46756715e-05, 1.31571032e-05], [4.35817747e-05, 3.43492829e-05, 9.33161937e-06], [4.41295670e-05, 3.12332850e-05, 7.67203892e-06], [4.47235834e-05, 2.71723238e-05, 6.19583034e-06], [4.54743567e-05, 2.19780250e-05, 5.31574733e-06], [4.57708011e-05, 1.73569697e-05, 4.65225321e-06], [4.60082410e-05, 1.53941182e-05, 4.28469411e-06], [4.54157052e-05, 1.37252200e-05, 4.09263794e-06], [4.43821191e-05, 1.29597693e-05, 4.18236550e-06], [4.52992681e-05, 1.17703157e-05, 3.95249845e-06], [4.71115054e-05, 1.77631937e-05, 3.62751052e-06], [4.60290321e-05, 2.39401288e-05, 4.93487551e-06], [4.58545837e-05, 2.56217081e-05, 5.63887988e-06], [4.60243464e-05, 2.62969115e-05, 7.22560526e-06], [4.58321811e-05, 2.66395800e-05, 8.58258500e-06], [4.60509145e-05, 2.69951252e-05, 9.79544802e-06], [4.51421438e-05, 2.74656868e-05, 1.03494922e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float323.263 1.133 ... 0.2238 0.2969
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[3.2628639 , 1.132586 , 0.73984253], [3.2539244 , 1.1218415 , 0.6636562 ], [3.3077629 , 1.105064 , 0.657457 ], [3.199039 , 1.1237029 , 0.6516208 ], [3.407606 , 1.1350415 , 0.6307573 ], [3.551251 , 1.1491935 , 0.61930174], [3.7079988 , 1.1682833 , 0.6382069 ], [3.6843374 , 1.1969991 , 0.59546936], [3.6373034 , 1.1951932 , 0.58847064], [3.5286233 , 1.1990137 , 0.58950627], [3.5112283 , 1.2090486 , 0.5561185 ], [3.5832782 , 1.1928139 , 0.55407566], [3.6162767 , 1.197721 , 0.57535815], [3.6732585 , 1.2360073 , 0.6680939 ], [3.7434201 , 1.285768 , 0.6698258 ], [3.9210746 , 1.3179896 , 0.7200737 ], [4.1534557 , 1.3624804 , 0.72308815], [4.350609 , 1.3700106 , 0.72829914], [4.34737 , 1.4054053 , 0.74411196], [4.3841596 , 1.3435694 , 0.69021 ], ... [0.24822167, 0.2229091 , 0.22997776], [0.2502671 , 0.21606663, 0.2836377 ], [0.25094187, 0.20619413, 0.33462837], [0.25031978, 0.20203003, 0.39669967], [0.247252 , 0.20765834, 0.42320406], [0.24486798, 0.21742085, 0.43833 ], [0.2399987 , 0.22879753, 0.45103633], [0.2378625 , 0.24977483, 0.47837028], [0.23650162, 0.26490203, 0.50369376], [0.23604476, 0.27550876, 0.52109915], [0.2333923 , 0.27310133, 0.5113795 ], [0.23511393, 0.2857891 , 0.5116854 ], [0.19609238, 0.18043011, 0.53184634], [0.2021423 , 0.17112611, 0.33909 ], [0.20476255, 0.18330167, 0.29207003], [0.21014665, 0.19243236, 0.28591996], [0.21374545, 0.20258361, 0.2771893 ], [0.21875525, 0.21056795, 0.28172302], [0.2241939 , 0.21769346, 0.2848692 ], [0.23144257, 0.22380565, 0.29694226]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float323.261 1.081 0.6602 ... 0.1893 0.283
- long_name :
- $Ri^g_T$
array([[[3.2613535 , 1.0807239 , 0.6601844 ], [3.2607088 , 1.0806876 , 0.6376095 ], [3.2541394 , 1.0787339 , 0.62403697], [3.1573467 , 1.095553 , 0.6195335 ], [3.33064 , 1.1045685 , 0.60020334], [3.4112148 , 1.1177173 , 0.57872057], [3.598929 , 1.1484542 , 0.59502447], [3.4866464 , 1.1638496 , 0.5739601 ], [3.478894 , 1.173312 , 0.5573975 ], [3.3537366 , 1.1519613 , 0.5667412 ], [3.2960985 , 1.1518707 , 0.5466292 ], [3.4085515 , 1.1364806 , 0.541849 ], [3.360477 , 1.1453516 , 0.56916815], [3.5060325 , 1.147816 , 0.64065945], [3.6237063 , 1.215729 , 0.6510779 ], [3.7974987 , 1.2563922 , 0.6879902 ], [4.037156 , 1.298629 , 0.6786159 ], [4.2002525 , 1.3074436 , 0.68557954], [4.2329836 , 1.3236547 , 0.71324885], [4.2080054 , 1.3151736 , 0.6461337 ], ... [0.19615808, 0.17713511, 0.17934614], [0.19791266, 0.16535255, 0.1786004 ], [0.19866161, 0.15758038, 0.18220627], [0.19679716, 0.14933537, 0.21518406], [0.19358617, 0.1441788 , 0.2393373 ], [0.18668334, 0.14489259, 0.255283 ], [0.18088251, 0.14877748, 0.27127427], [0.17411648, 0.15260799, 0.2903708 ], [0.16779342, 0.16068009, 0.30303138], [0.16678639, 0.16395637, 0.3173359 ], [0.16421464, 0.16863132, 0.3253452 ], [0.16048996, 0.17333059, 0.32974294], [0.15450153, 0.14349043, 0.34493858], [0.15779564, 0.14688559, 0.29000515], [0.1605247 , 0.1545775 , 0.25673643], [0.16559987, 0.16310802, 0.27112243], [0.17053735, 0.17055264, 0.2761741 ], [0.17479429, 0.1770179 , 0.27965385], [0.18131071, 0.18408577, 0.27241796], [0.18530713, 0.18929055, 0.2829873 ]]], dtype=float32) - tau(hour, tau_bins)float320.02975 0.05414 ... 0.05437 0.08353
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02975217, 0.05414128, 0.08337677], [0.02997051, 0.05396286, 0.08334547], [0.02989443, 0.05379027, 0.08344196], [0.02965429, 0.05390463, 0.08429465], [0.02966079, 0.05406607, 0.08407371], [0.02973503, 0.05454572, 0.08423293], [0.02972214, 0.05489863, 0.08463559], [0.02960968, 0.05482671, 0.08451898], [0.02952631, 0.05462506, 0.08395986], [0.02949316, 0.05446932, 0.08439989], [0.02946503, 0.05439705, 0.08432356], [0.0292913 , 0.05438457, 0.08425272], [0.02905069, 0.05423319, 0.08445528], [0.02918139, 0.05503562, 0.08494085], [0.02949452, 0.0555568 , 0.08575647], [0.02982584, 0.05614248, 0.08632967], [0.03012207, 0.05678497, 0.08614163], [0.0303158 , 0.05744474, 0.08672108], [0.03049818, 0.05776399, 0.08675309], [0.03012105, 0.05725272, 0.08582553], [0.03001658, 0.05665065, 0.08521383], [0.03018327, 0.05611375, 0.08581671], [0.02999875, 0.05514082, 0.08425651], [0.02983666, 0.05437292, 0.08352954]], dtype=float32)
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0001793 eps (depth, hour, tau_bins) float32 1.167e-10 1.08e-09 ... 1.188e-08 chi (depth, hour, tau_bins) float32 1.739e-08 2.265e-08 ... 2.921e-09 Jb (depth, hour, tau_bins) float32 -2.555e-10 ... -2.291e-11 Jq (depth, hour, tau_bins) float64 -0.4038 -0.4617 ... -30.73 -1.899 S2 (depth, hour, tau_bins) float32 6.214e-05 0.0002147 ... 3.608e-05 N2 (depth, hour, tau_bins) float32 0.0002396 0.0002716 ... 1.035e-05 Rig (depth, hour, tau_bins) float32 3.263 1.133 ... 0.2238 0.2969 Rig_T (depth, hour, tau_bins) float32 3.261 1.081 ... 0.1893 0.283 tau (hour, tau_bins) float32 0.02975 0.05414 ... 0.05437 0.08353 Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 1.001e-06
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062596e-06], [1.00062630e-06, 1.00062630e-06, 1.00062596e-06], [1.00062630e-06, 1.00062618e-06, 1.00062596e-06], [1.00062630e-06, 1.00062618e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062630e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], ... [3.81811027e-04, 2.90885218e-04, 1.71408691e-02], [4.25665407e-04, 6.40261802e-04, 4.36169878e-02], [4.42612451e-04, 1.11911795e-03, 5.60436398e-02], [4.89161233e-04, 5.52093564e-03, 7.04844594e-02], [5.36391861e-04, 1.31646777e-02, 7.70594999e-02], [6.37809746e-04, 2.09450833e-02, 8.12212825e-02], [7.49726663e-04, 2.86364648e-02, 8.68151113e-02], [9.01583699e-04, 3.39822620e-02, 8.97497833e-02], [1.04411470e-03, 3.73836383e-02, 9.08122957e-02], [1.13334751e-03, 3.80024388e-02, 8.79565999e-02], [1.30286883e-03, 4.09585051e-02, 8.61921236e-02], [1.38791348e-03, 4.12570350e-02, 8.57141986e-02], [8.68165516e-04, 1.87469891e-03, 2.38270964e-02], [7.35677429e-04, 2.46586249e-04, 1.61692663e-03], [8.00757844e-04, 9.72932903e-05, 2.39668181e-03], [7.69015285e-04, 3.98732591e-05, 6.91609748e-04], [7.04221777e-04, 2.61289642e-05, 3.45671833e-05], [6.36181561e-04, 8.17534328e-06, 1.58894795e-06], [5.70160919e-04, 3.65733058e-06, 1.00068951e-06], [5.23375580e-04, 1.35793562e-06, 1.00068837e-06]]], dtype=float32) - eps(depth, hour, tau_bins)float325.735e-10 5.908e-08 ... 1.889e-09
- long_name :
- $SP$
- units :
- W/kg
array([[[5.73483705e-10, 5.90838596e-08, 2.15448935e-07], [5.81091175e-10, 5.87181788e-08, 2.12549949e-07], [6.81909473e-10, 5.76417030e-08, 2.00810078e-07], [6.64125366e-10, 5.87719846e-08, 1.89282304e-07], [5.68308345e-10, 5.55936523e-08, 1.68704887e-07], [4.92745234e-10, 4.92134227e-08, 1.76220226e-07], [4.96048647e-10, 4.67140282e-08, 1.57617066e-07], [5.01904074e-10, 4.51418529e-08, 1.46062305e-07], [5.14162712e-10, 4.29492850e-08, 1.45271159e-07], [5.65038460e-10, 4.10675156e-08, 1.46149574e-07], [5.86884707e-10, 4.17339230e-08, 1.49086731e-07], [6.35932196e-10, 4.17201989e-08, 1.53073813e-07], [6.37232100e-10, 4.25185576e-08, 1.53210635e-07], [6.10381190e-10, 3.85168306e-08, 1.55136433e-07], [5.65663461e-10, 3.38582353e-08, 1.52575666e-07], [5.15196330e-10, 3.19983080e-08, 1.39071460e-07], [4.52812149e-10, 2.97344300e-08, 1.87967046e-07], [4.13996726e-10, 2.86390218e-08, 1.96979386e-07], [3.91505439e-10, 2.65191584e-08, 1.93616984e-07], [3.97265304e-10, 3.43650264e-08, 2.04911160e-07], ... [5.93399214e-08, 6.42338165e-08, 2.96590201e-07], [6.45967404e-08, 1.58682056e-07, 3.79704971e-07], [7.07415992e-08, 2.60141974e-07, 3.58420380e-07], [8.38285246e-08, 3.46018254e-07, 2.93743597e-07], [1.02582291e-07, 3.65818039e-07, 2.58425359e-07], [1.33359805e-07, 3.55552288e-07, 2.17777028e-07], [1.73986905e-07, 3.15978582e-07, 1.88055054e-07], [2.09051464e-07, 2.73394363e-07, 1.64039164e-07], [2.38001761e-07, 2.25841461e-07, 1.45215409e-07], [2.31611693e-07, 2.02772654e-07, 1.26571138e-07], [2.34431269e-07, 1.88707361e-07, 1.14071625e-07], [2.39257758e-07, 1.72018076e-07, 1.11745550e-07], [1.18438180e-07, 4.03291729e-08, 4.43827801e-08], [1.16053108e-07, 1.05902611e-08, 8.01906808e-09], [1.20561225e-07, 1.02162936e-08, 1.02953468e-08], [1.12213129e-07, 9.34675537e-09, 5.69340086e-09], [1.01487046e-07, 7.98773758e-09, 2.47878096e-09], [8.98038195e-08, 7.39516626e-09, 1.49280566e-09], [8.13782322e-08, 7.12342629e-09, 1.23643273e-09], [7.33811589e-08, 7.11201409e-09, 1.88859817e-09]]], dtype=float32) - chi(depth, hour, tau_bins)float322.421e-08 4.218e-08 ... 1.773e-11
- long_name :
- $χ$
- units :
- C^2/s
array([[[2.42052565e-08, 4.21824211e-08, 6.13486506e-08], [2.43459812e-08, 4.24095639e-08, 6.18638438e-08], [2.49258587e-08, 4.28722551e-08, 6.38544719e-08], [2.41345166e-08, 4.36538734e-08, 6.12625897e-08], [2.41304896e-08, 4.32199982e-08, 6.58363177e-08], [2.39560300e-08, 4.23458459e-08, 6.48738592e-08], [2.39092675e-08, 4.18693453e-08, 6.48294289e-08], [2.36714595e-08, 4.21219220e-08, 6.36250235e-08], [2.35294735e-08, 4.19933741e-08, 6.35801527e-08], [2.38001778e-08, 4.14795842e-08, 6.10450712e-08], [2.42841285e-08, 4.09588949e-08, 6.14152711e-08], [2.45673526e-08, 4.12250003e-08, 6.06932744e-08], [2.50661039e-08, 4.04394562e-08, 6.33500861e-08], [2.45900935e-08, 4.08110026e-08, 6.14272153e-08], [2.40473650e-08, 3.92472401e-08, 6.08052844e-08], [2.33138522e-08, 3.92737824e-08, 5.67299274e-08], [2.31825688e-08, 3.83459664e-08, 5.78424597e-08], [2.29814709e-08, 3.78962568e-08, 5.81310964e-08], [2.24128005e-08, 3.67799586e-08, 5.87707021e-08], [2.26952821e-08, 3.83477641e-08, 5.96626819e-08], ... [6.58048833e-08, 3.45897888e-08, 3.06971515e-07], [7.74475311e-08, 1.30058410e-07, 4.16388218e-07], [8.59298765e-08, 2.74952754e-07, 3.99758875e-07], [9.98236942e-08, 3.31715938e-07, 2.91729918e-07], [1.24177078e-07, 3.48626457e-07, 2.22114693e-07], [1.59281512e-07, 3.29062061e-07, 1.67315164e-07], [1.86461662e-07, 2.82267621e-07, 1.27866073e-07], [2.02538843e-07, 2.26290396e-07, 9.58634558e-08], [2.10553225e-07, 1.79261093e-07, 8.23487909e-08], [1.90766357e-07, 1.44952992e-07, 7.88916452e-08], [1.84443806e-07, 1.37191520e-07, 7.31033865e-08], [1.62389298e-07, 1.18587856e-07, 7.63602017e-08], [8.45517505e-08, 9.69671721e-09, 2.11790869e-08], [7.38182564e-08, 1.38504741e-09, 1.56564184e-09], [1.16998599e-07, 9.71107417e-10, 3.63399155e-09], [1.27962963e-07, 5.68661396e-10, 2.04201012e-09], [1.21315836e-07, 4.69920158e-10, 1.60213856e-10], [1.14172195e-07, 1.94918595e-10, 2.22373005e-11], [1.04255875e-07, 1.51198110e-10, 1.14214176e-11], [9.37985618e-08, 1.06092787e-10, 1.77340642e-11]]], dtype=float32) - Jb(depth, hour, tau_bins)float32-3.165e-10 ... -5.549e-12
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[-3.16469878e-10, -4.06335215e-10, -4.95005370e-10], [-3.17809973e-10, -4.08817480e-10, -5.05972153e-10], [-3.19277160e-10, -4.06790518e-10, -5.20859522e-10], [-3.16120546e-10, -4.13883317e-10, -5.00933961e-10], [-3.13139681e-10, -4.10488865e-10, -5.20764376e-10], [-3.10633408e-10, -4.08861972e-10, -5.17567100e-10], [-3.07887188e-10, -4.05906087e-10, -5.04892905e-10], [-3.05545256e-10, -4.04478839e-10, -5.07665687e-10], [-3.04483549e-10, -4.05523504e-10, -5.02378528e-10], [-3.06875358e-10, -4.05313921e-10, -4.88781682e-10], [-3.09053727e-10, -4.06532308e-10, -4.90021246e-10], [-3.14069798e-10, -4.04227846e-10, -4.88690088e-10], [-3.19417853e-10, -4.00812328e-10, -4.95343877e-10], [-3.16587395e-10, -4.00275202e-10, -4.89477736e-10], [-3.14177545e-10, -3.95527555e-10, -4.95618879e-10], [-3.08587988e-10, -3.92145816e-10, -4.73352690e-10], [-3.05383857e-10, -3.87099630e-10, -4.77537287e-10], [-3.04662073e-10, -3.82146120e-10, -4.79010498e-10], [-3.03895215e-10, -3.79181186e-10, -4.81312434e-10], [-3.04242853e-10, -3.84696941e-10, -4.91413632e-10], ... [-5.13596010e-09, -1.12433076e-08, -1.73453998e-07], [-6.16254914e-09, -2.75167942e-08, -1.78586021e-07], [-7.04082215e-09, -5.64420901e-08, -1.63006959e-07], [-9.40332612e-09, -8.36426182e-08, -1.39608161e-07], [-1.21739756e-08, -9.57661683e-08, -1.16583863e-07], [-1.44949128e-08, -9.81782833e-08, -1.04734198e-07], [-1.82505584e-08, -9.62569473e-08, -9.39466602e-08], [-2.09147721e-08, -8.83932714e-08, -8.06017368e-08], [-2.07831228e-08, -7.65039445e-08, -8.95265799e-08], [-2.29593340e-08, -7.25662659e-08, -7.88470302e-08], [-2.28492016e-08, -6.55638672e-08, -8.51448192e-08], [-7.32230188e-09, -3.86669319e-09, -2.06859028e-08], [-5.07420683e-09, -1.86237886e-10, -6.27351116e-10], [-8.44772785e-09, -7.74282166e-11, -2.03356487e-09], [-9.28062427e-09, -1.02767976e-11, -6.27712660e-10], [-8.80577300e-09, -6.66913183e-12, -3.52873321e-11], [-8.48780868e-09, -5.79612356e-12, -6.17024704e-12], [-7.54977414e-09, -5.89862187e-12, -4.79358827e-12], [-6.58689014e-09, -6.02329558e-12, -5.54850436e-12]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.4816 -0.6392 ... -0.01328
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[-4.81552918e-01, -6.39167771e-01, -7.62933746e-01], [-4.85329101e-01, -6.42421775e-01, -7.69579102e-01], [-4.90946456e-01, -6.40105755e-01, -7.79354780e-01], [-4.83578128e-01, -6.47227242e-01, -7.73838051e-01], [-4.83167888e-01, -6.42581872e-01, -7.91665006e-01], [-4.80104343e-01, -6.40745775e-01, -7.90478163e-01], [-4.77434282e-01, -6.38051315e-01, -7.81818421e-01], [-4.75500770e-01, -6.35332332e-01, -7.90931913e-01], [-4.73519131e-01, -6.37496725e-01, -7.95123686e-01], [-4.78122582e-01, -6.36972344e-01, -7.80516887e-01], [-4.82024922e-01, -6.37689752e-01, -7.90518737e-01], [-4.84282250e-01, -6.35676375e-01, -7.79288919e-01], [-4.88477602e-01, -6.29759740e-01, -7.87195326e-01], [-4.83797879e-01, -6.26795017e-01, -7.73909660e-01], [-4.78714276e-01, -6.17771603e-01, -7.74951022e-01], [-4.71088650e-01, -6.12925103e-01, -7.50714301e-01], [-4.70555356e-01, -6.07242223e-01, -7.45696696e-01], [-4.70414981e-01, -6.04348376e-01, -7.45931460e-01], [-4.67814145e-01, -5.97328799e-01, -7.44359541e-01], [-4.64213041e-01, -6.07979658e-01, -7.65086306e-01], ... [-1.52504881e+01, -1.08198466e+01, -2.40912335e+02], [-1.79780728e+01, -2.77116033e+01, -4.22594618e+02], [-1.87859441e+01, -6.95042618e+01, -4.35070729e+02], [-2.05550840e+01, -1.68302990e+02, -4.19219828e+02], [-2.43782413e+01, -2.28539469e+02, -3.79352246e+02], [-2.99324167e+01, -2.53416225e+02, -3.39421920e+02], [-3.62329256e+01, -2.54032476e+02, -3.03281977e+02], [-4.57524427e+01, -2.37408954e+02, -2.80372229e+02], [-5.52564399e+01, -2.23234615e+02, -2.56960106e+02], [-5.82335571e+01, -2.00110950e+02, -2.44827332e+02], [-6.62942474e+01, -1.96551320e+02, -2.38200016e+02], [-7.06634387e+01, -1.84589225e+02, -2.36158794e+02], [-2.57588716e+01, -1.98516862e+01, -7.16579682e+01], [-2.11297949e+01, -2.07265483e+00, -6.26872271e+00], [-2.92281227e+01, -1.13948511e+00, -1.02573476e+01], [-2.99932999e+01, -5.12616250e-01, -4.02535216e+00], [-2.89512937e+01, -3.15996434e-01, -2.26048104e-01], [-2.69464025e+01, -1.24594175e-01, -1.93938576e-02], [-2.45196400e+01, -7.30878555e-02, -1.04230675e-02], [-2.18747312e+01, -4.18680368e-02, -1.32777846e-02]]]) - S2(depth, hour, tau_bins)float320.0001418 0.0004672 ... 1.13e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.41836601e-04, 4.67205304e-04, 7.15640199e-04], [1.41885423e-04, 4.72498999e-04, 7.31437409e-04], [1.48016523e-04, 4.70162660e-04, 7.41417753e-04], [1.40774020e-04, 4.72693850e-04, 7.54935376e-04], [1.37023657e-04, 4.75256471e-04, 6.97829120e-04], [1.28246960e-04, 4.59599047e-04, 7.07112486e-04], [1.25261853e-04, 4.47610510e-04, 6.60455436e-04], [1.26983170e-04, 4.52060514e-04, 6.61135535e-04], [1.30664936e-04, 4.44407866e-04, 6.85080537e-04], [1.37154537e-04, 4.38653689e-04, 6.94098591e-04], [1.42129575e-04, 4.40310774e-04, 7.55701447e-04], [1.44565871e-04, 4.36307222e-04, 7.79487425e-04], [1.45691098e-04, 4.47019353e-04, 7.87896803e-04], [1.44280799e-04, 4.34455666e-04, 7.75266206e-04], [1.38319811e-04, 4.21505451e-04, 7.35988317e-04], [1.30991262e-04, 4.10690991e-04, 7.31264823e-04], [1.23740640e-04, 3.95052601e-04, 7.36095477e-04], [1.19035845e-04, 3.91300389e-04, 7.24209938e-04], [1.12783542e-04, 3.89472814e-04, 7.19981967e-04], [1.15830204e-04, 4.13761125e-04, 7.08727632e-04], ... [1.48438034e-04, 8.40546709e-05, 3.76138487e-05], [1.49744868e-04, 8.34067832e-05, 2.62681206e-05], [1.55048183e-04, 8.11126229e-05, 1.71083029e-05], [1.57887640e-04, 7.14546768e-05, 1.13211072e-05], [1.63779288e-04, 5.51105259e-05, 8.39280438e-06], [1.68770872e-04, 4.05372193e-05, 6.78651486e-06], [1.68515748e-04, 2.62506983e-05, 5.61330125e-06], [1.69917534e-04, 1.88715403e-05, 4.70684881e-06], [1.62872253e-04, 1.40121865e-05, 4.11091787e-06], [1.63078847e-04, 1.16760530e-05, 3.64465927e-06], [1.55001995e-04, 1.02589747e-05, 3.68917995e-06], [1.50725929e-04, 9.34424224e-06, 3.74268780e-06], [1.62076351e-04, 1.31826091e-05, 3.24889447e-06], [1.72409025e-04, 1.79971466e-05, 4.08658934e-06], [1.71353196e-04, 2.51919682e-05, 4.80949393e-06], [1.65656005e-04, 3.08949420e-05, 5.40391466e-06], [1.60071344e-04, 3.37284146e-05, 6.34383969e-06], [1.54592650e-04, 3.72321556e-05, 7.46843898e-06], [1.50900625e-04, 4.07821572e-05, 8.78149694e-06], [1.46861552e-04, 4.46950471e-05, 1.12951284e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002504 0.0002843 ... 7.772e-06
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.50380457e-04, 2.84322537e-04, 3.26497160e-04], [2.49794335e-04, 2.84366863e-04, 3.40093946e-04], [2.50732730e-04, 2.82385619e-04, 3.52266827e-04], [2.48224998e-04, 2.84529960e-04, 3.56787234e-04], [2.50212091e-04, 2.83362053e-04, 3.48632922e-04], [2.50026409e-04, 2.83005007e-04, 3.47889756e-04], [2.52470782e-04, 2.82140682e-04, 3.33684904e-04], [2.52225524e-04, 2.83426692e-04, 3.28114023e-04], [2.52644822e-04, 2.83705245e-04, 3.10908479e-04], [2.55907566e-04, 2.84160778e-04, 2.99137144e-04], [2.56487081e-04, 2.83852918e-04, 2.97512830e-04], [2.59825640e-04, 2.79677886e-04, 3.08105693e-04], [2.58581596e-04, 2.79740925e-04, 3.08726507e-04], [2.58143933e-04, 2.80908833e-04, 3.07591661e-04], [2.53710255e-04, 2.79614876e-04, 3.14174657e-04], [2.53522012e-04, 2.79249914e-04, 3.10601346e-04], [2.52498110e-04, 2.75294151e-04, 3.15695826e-04], [2.51230667e-04, 2.75489874e-04, 3.10448901e-04], [2.47999094e-04, 2.76833016e-04, 3.11023148e-04], [2.47006770e-04, 2.79002707e-04, 3.11012758e-04], ... [4.00318095e-05, 2.19094200e-05, 1.75809691e-05], [4.08545384e-05, 2.29993839e-05, 1.44682808e-05], [4.17602168e-05, 2.32393431e-05, 1.14376789e-05], [4.19672360e-05, 2.17431643e-05, 7.65365985e-06], [4.25652397e-05, 1.95154098e-05, 5.98406086e-06], [4.24695463e-05, 1.64385110e-05, 5.06878587e-06], [4.20580618e-05, 1.29026885e-05, 4.27718123e-06], [4.17472147e-05, 9.78873140e-06, 3.76438538e-06], [4.05107130e-05, 7.85583507e-06, 3.40888255e-06], [3.98970660e-05, 6.68765961e-06, 3.17039030e-06], [3.72795621e-05, 6.00186786e-06, 3.05350545e-06], [3.63023901e-05, 5.69715758e-06, 3.10032669e-06], [3.58221514e-05, 5.26593840e-06, 2.91269725e-06], [3.86316751e-05, 5.47839863e-06, 2.88081537e-06], [4.08812521e-05, 7.10814311e-06, 3.56441478e-06], [4.26108927e-05, 8.30356112e-06, 4.09821496e-06], [4.13776215e-05, 9.73325223e-06, 4.94596861e-06], [4.03243102e-05, 1.09723651e-05, 5.75752665e-06], [4.00129029e-05, 1.23583932e-05, 6.63946867e-06], [3.99043274e-05, 1.35408591e-05, 7.77176410e-06]]], dtype=float32) - Rig(depth, hour, tau_bins)float321.384 0.4356 ... 0.3581 0.6224
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[1.3843784 , 0.4355776 , 0.3091246 ], [1.3862953 , 0.42921245, 0.29979953], [1.3186188 , 0.43328127, 0.29791135], [1.319762 , 0.431867 , 0.31552285], [1.3876023 , 0.42956513, 0.33238146], [1.4713855 , 0.43854415, 0.33611113], [1.4585589 , 0.44340074, 0.3397169 ], [1.422827 , 0.44158596, 0.33740765], [1.4308827 , 0.44296315, 0.3283518 ], [1.3624867 , 0.44709092, 0.32959506], [1.3321421 , 0.44995126, 0.31895676], [1.3095046 , 0.44489077, 0.31756058], [1.299477 , 0.44661266, 0.31964993], [1.3353751 , 0.45561314, 0.32711515], [1.3878534 , 0.46209648, 0.33242503], [1.4295163 , 0.47146723, 0.3417432 ], [1.5292119 , 0.48259148, 0.33962864], [1.5888655 , 0.49773607, 0.3420213 ], [1.6786402 , 0.50815296, 0.34517553], [1.6653159 , 0.49143514, 0.32707235], ... [0.28946295, 0.31139803, 0.48474592], [0.28830624, 0.31103155, 0.6091647 ], [0.28619725, 0.31105167, 0.6668595 ], [0.27883843, 0.3508357 , 0.70064497], [0.27638355, 0.40184402, 0.73615754], [0.2694257 , 0.4452605 , 0.747144 ], [0.2700301 , 0.5024443 , 0.76513684], [0.27028793, 0.53571266, 0.8011063 ], [0.27429098, 0.5733974 , 0.8354088 ], [0.27542618, 0.5895082 , 0.85250646], [0.27886292, 0.59905994, 0.8383981 ], [0.27949739, 0.61504954, 0.85223484], [0.23919442, 0.4293044 , 0.8984655 ], [0.23977003, 0.3274899 , 0.7073171 ], [0.24828433, 0.31492984, 0.70929885], [0.2581412 , 0.31994987, 0.7032059 ], [0.2622922 , 0.32738152, 0.69430584], [0.26598185, 0.33688056, 0.6817956 ], [0.27302545, 0.34765816, 0.68264323], [0.28132206, 0.35813454, 0.6223506 ]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float321.343 0.4258 0.304 ... 0.297 0.5564
- long_name :
- $Ri^g_T$
array([[[1.3434284 , 0.42575198, 0.30399483], [1.339348 , 0.42032528, 0.2998174 ], [1.2688062 , 0.42568558, 0.3012114 ], [1.2518506 , 0.42318174, 0.3039454 ], [1.3326304 , 0.42496133, 0.3102635 ], [1.4218832 , 0.43632212, 0.31440943], [1.384247 , 0.44214174, 0.33351892], [1.3839062 , 0.4455327 , 0.32989636], [1.3978841 , 0.4521952 , 0.32683736], [1.3210678 , 0.4536286 , 0.31877822], [1.2479538 , 0.4600122 , 0.3171023 ], [1.2329819 , 0.45810977, 0.31853944], [1.244715 , 0.45624778, 0.31939033], [1.2971063 , 0.46135846, 0.32418156], [1.3399007 , 0.46895933, 0.33118114], [1.3868494 , 0.47377062, 0.34303665], [1.536852 , 0.4890711 , 0.33796883], [1.5975964 , 0.49740615, 0.33952767], [1.6472256 , 0.50301504, 0.33876863], [1.6406693 , 0.49364513, 0.3232821 ], ... [0.21892303, 0.24460581, 0.2782022 ], [0.21715575, 0.23441526, 0.3032993 ], [0.21339254, 0.22937962, 0.33986917], [0.20731284, 0.22571316, 0.3779726 ], [0.20430309, 0.23617734, 0.4060605 ], [0.19837219, 0.24865082, 0.4239027 ], [0.19388697, 0.27108073, 0.44667554], [0.19081205, 0.2972318 , 0.46966553], [0.18402362, 0.32012552, 0.47574738], [0.18582553, 0.3224433 , 0.5086578 ], [0.18361391, 0.34084806, 0.52148837], [0.18255128, 0.34092087, 0.52430046], [0.17780563, 0.27581286, 0.59936225], [0.18213266, 0.26158136, 0.53423965], [0.18586144, 0.25454444, 0.54415107], [0.19341025, 0.26411104, 0.5709722 ], [0.19832414, 0.2755742 , 0.5958633 ], [0.20684622, 0.28239402, 0.59805214], [0.21064335, 0.29060137, 0.5932599 ], [0.2162599 , 0.29699275, 0.5563707 ]]], dtype=float32) - tau(hour, tau_bins)float320.02927 0.0545 ... 0.05481 0.08379
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02926666, 0.05449591, 0.08384066], [0.02953365, 0.05412898, 0.08342438], [0.02950766, 0.0540043 , 0.08344106], [0.02929587, 0.05410497, 0.08466474], [0.0292785 , 0.05410444, 0.08424635], [0.02926119, 0.05435768, 0.08391857], [0.02946588, 0.05500093, 0.08486678], [0.02949753, 0.05470305, 0.08457853], [0.02931711, 0.05448961, 0.08386523], [0.02923332, 0.05441001, 0.08456027], [0.02923119, 0.05438141, 0.08475135], [0.02906845, 0.05451858, 0.0846531 ], [0.02867212, 0.05450471, 0.08504945], [0.02912105, 0.05511454, 0.0853009 ], [0.02948293, 0.05570985, 0.08595277], [0.0298175 , 0.05617661, 0.08633688], [0.030358 , 0.0568147 , 0.08630857], [0.0307796 , 0.05742799, 0.08671759], [0.03049245, 0.05769216, 0.08660597], [0.0303795 , 0.05715569, 0.08534982], [0.03001719, 0.05657351, 0.08543649], [0.03012108, 0.05630453, 0.08567342], [0.02991865, 0.05550613, 0.08454789], [0.02963275, 0.05481243, 0.08379018]], dtype=float32)
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 1.001e-06 eps (depth, hour, tau_bins) float32 5.735e-10 5.908e-08 ... 1.889e-09 chi (depth, hour, tau_bins) float32 2.421e-08 4.218e-08 ... 1.773e-11 Jb (depth, hour, tau_bins) float32 -3.165e-10 ... -5.549e-12 Jq (depth, hour, tau_bins) float64 -0.4816 -0.6392 ... -0.01328 S2 (depth, hour, tau_bins) float32 0.0001418 0.0004672 ... 1.13e-05 N2 (depth, hour, tau_bins) float32 0.0002504 0.0002843 ... 7.772e-06 Rig (depth, hour, tau_bins) float32 1.384 0.4356 ... 0.3581 0.6224 Rig_T (depth, hour, tau_bins) float32 1.343 0.4258 ... 0.297 0.5564 tau (hour, tau_bins) float32 0.02927 0.0545 ... 0.05481 0.08379 Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
seasonal cycle#
tree = mixpods.persist_tree(tree)
Validate before continuing#
mixpods.validate_tree(tree)
TODO: Verify depth is normalized#
for name, ds in datasets.items():
(ds.cf["sea_water_x_velocity"].cf["Z"].plot(marker=".", ls="none", label=name))
plt.legend()
<matplotlib.legend.Legend>
Mean profiles#
A lot more shear, \(S, S^2\) just above the EUC when visc is turned down!
We are very slightly lower on \(S^2\), \(N_T^2\) in the top 80m, compare
TAO,kpp.lmd.004,new_baseline.kpp.lmd.004.Using the standard
Ri_c=0.3, so deeper KPP surface layer, decreases \(S^2\), \(N_T^2\) in the top 60m.new_baseline.kpp.lmd.004vsnew_baseline.kpp.lmd.005
for context, Peters et al (1995) is interesting:
Variability patterns at 50-350 m are distinctly different from the upper 50 m containing the diurnal cycle of mixing
Large-scale shear of wavenumbers k < 0.01 cpm is associated with the slowly varying EUC and EIC. Large-scale is the dominant component of total shear above 50 m and in the thermostad, 170-270 m.
Fine-scale shear, 0.01cpm < k < 0.5 cpm,provides the dominant component of total rms shear and exceeds the large-scale component in thick layersaroundthe coresof EUC and EIC, where Fr < 1, at 50-170 m and below 270 m.
Variations of fine-scale shear cause variations in turbulent mixing; the large-scaleshear alone is a poor predictor of mixing.
Lacking a model that links large-scale, fine-scale, and turbulent flow components,our service to equatorial modelers consists of describing general levels of eddy diffusivities and their variability patterns.
These models should not be resolving “finescale” shear, and the mixing is not well correlated with “large-scale shear”, so we need a “background” diffusivity/viscosity to make things work.
But see that std(\(S^2\)) is a lot stronger above the EUC core, relative to TAO for new_baseline.kpp.lmd.004 and new_baseline.kpp.lmd.005
%autoreload
S2 = mixpods.plot_profile_fill(tree, "S2", "S^2")
N2 = mixpods.plot_profile_fill(tree, "N2T", "N_T^2")
u = mixpods.plot_profile_fill(tree, "sea_water_x_velocity", "u")
T = mixpods.plot_profile_fill(tree, "sea_water_potential_temperature", "T")
Ri = mixpods.plot_median_Ri(tree)
(S2 + N2 + Ri + u + T).cols(3)
Stability Diagram#
Major change in La-Nina Warming for the new-baseline runs. We need to check ONI closely.
%autoreload
mixpods.plot_stability_diagram_by_dataset(tree, nrows=2)
Daily composites#
Hard to interpret! I think a lot of this is bias in KPP surface layer vs actively mixing layer in obs. We can write better diagnostics to check this (Moum et al, 2023)
The 89m χpod comparison is quite interesting. Suggests we do need more background visc
%autoreload
mixpods.plot_daily_composites(dailies, ["eps", "KT"], logy=True, legend=False).opts(
hv.opts.GridSpace(show_legend=True)
)
%autoreload
mixpods.plot_daily_composites(
dailies, ["S2", "N2", "Rig_T"], logy=False, legend=False
).opts(hv.opts.GridSpace(show_legend=True, width=200), hv.opts.Overlay(frame_width=200))
Turbulence distributions#
handles = [
mixpods.plot_distributions(tree, "chi", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(tree, "eps", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(
tree, "ocean_vertical_heat_diffusivity", bins=np.linspace(-8, -1, 101), log=True
),
# plot_distributions(tree, "Jq", bins=np.linspace(-1000, 0, 51), log=False),
mixpods.plot_distributions(tree, "Rig_T", np.linspace(-0.5, 1.5, 61))
* hv.VLine(0.25).opts(line_color="k"),
]
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:769: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:769: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
hv.Layout(handles).opts("Overlay", frame_width=600).cols(2)
Compare boundary layer depth#
mixing_layer_depth_criteria = {
"boundary_layer_depth": {"name": "KPPhbl|KPP_OBLdepth|ePBL_h_ML"},
}
hbl = (
tree.drop_nodes("TAO")
.dc.subset_nodes("KPP_OBLdepth")
.dc.concatenate_nodes()
.reset_coords(drop=True)
).load()
(
# hbl.groupby("time.hour").mean().hvplot.line(by="node", flip_yaxis=True)
hbl.groupby("time.hour").median().hvplot.line(by="node", flip_yaxis=True)
+ hbl.to_dataframe().hvplot.hist(
by="node",
bins=np.arange(0, 90, 1),
normed=1,
alpha=0.3,
ylim=(0, 0.05),
muted_alpha=0,
)
).opts(hv.opts.Curve(invert_yaxis=True))
Heat Budget#
f, ax = plt.subplots(
2,
math.ceil(len(tree) / 2),
constrained_layout=True,
squeeze=False,
sharex=True,
sharey=True,
figsize=(10, 3),
)
for axx, (name, node) in zip(ax.flat, tree.children.items()):
mixpods.plot_climo_heat_budget_1d(node.ds, mxldepth=-40, penetration="mom", ax=axx)
axx.set_title(name)
dcpy.plots.clean_axes(ax)